Relevant bibliographies by topics / Gradient-based model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Gradient-based model'

Author: Grafiati
Published: 17 May 2025

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Journal articles on the topic "Gradient-based model"

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Burrows, Wesley, and John Doherty. "Gradient-based model calibration with proxy-model assistance." Journal of Hydrology 533 (February 2016): 114–27. http://dx.doi.org/10.1016/j.jhydrol.2015.11.033.

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D'Oro, Pierluca, Alberto Maria Metelli, Andrea Tirinzoni, Matteo Papini, and Marcello Restelli. "Gradient-Aware Model-Based Policy Search." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3801–8. http://dx.doi.org/10.1609/aaai.v34i04.5791.

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Traditional model-based reinforcement learning approaches learn a model of the environment dynamics without explicitly considering how it will be used by the agent. In the presence of misspecified model classes, this can lead to poor estimates, as some relevant available information is ignored. In this paper, we introduce a novel model-based policy search approach that exploits the knowledge of the current agent policy to learn an approximate transition model, focusing on the portions of the environment that are most relevant for policy improvement. We leverage a weighting scheme, derived from the minimization of the error on the model-based policy gradient estimator, in order to define a suitable objective function that is optimized for learning the approximate transition model. Then, we integrate this procedure into a batch policy improvement algorithm, named Gradient-Aware Model-based Policy Search (GAMPS), which iteratively learns a transition model and uses it, together with the collected trajectories, to compute the new policy parameters. Finally, we empirically validate GAMPS on benchmark domains analyzing and discussing its properties.
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Estrin, Y., B. Sluys, Y. Brechet, and A. Molinari. "A dislocation based gradient plasticity model." Le Journal de Physique IV 08, PR8 (November 1998): Pr8–135—Pr8–141. http://dx.doi.org/10.1051/jp4:1998817.

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Wallin, Mathias, and Matti Ristinmaa. "Deformation gradient based kinematic hardening model." International Journal of Plasticity 21, no. 10 (October 2005): 2025–50. http://dx.doi.org/10.1016/j.ijplas.2005.01.007.

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Kaleta, Małgorzata P., Remus G. Hanea, Arnold W. Heemink, and Jan-Dirk Jansen. "Model-reduced gradient-based history matching." Computational Geosciences 15, no. 1 (August 5, 2010): 135–53. http://dx.doi.org/10.1007/s10596-010-9203-5.

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Brinckmann, Steffen, Thomas Siegmund, and Yonggang Huang. "A dislocation density based strain gradient model." International Journal of Plasticity 22, no. 9 (September 2006): 1784–97. http://dx.doi.org/10.1016/j.ijplas.2006.01.005.

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Liu, Pengfei. "Pansharpening with transform-based gradient transferring model." IET Image Processing 13, no. 13 (November 14, 2019): 2614–22. http://dx.doi.org/10.1049/iet-ipr.2018.6080.

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Bengio, Yoshua. "Gradient-Based Optimization of Hyperparameters." Neural Computation 12, no. 8 (August 1, 2000): 1889–900. http://dx.doi.org/10.1162/089976600300015187.

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Many machine learning algorithms can be formulated as the minimization of a training criterion that involves a hyperparameter. This hyperparameter is usually chosen by trial and error with a model selection criterion. In this article we present a methodology to optimize several hyper-parameters, based on the computation of the gradient of a model selection criterion with respect to the hyperparameters. In the case of a quadratic training criterion, the gradient of the selection criterion with respect to the hyperparameters is efficiently computed by backpropagating through a Cholesky decomposition. In the more general case, we show that the implicit function theorem can be used to derive a formula for the hyper-parameter gradient involving second derivatives of the training criterion.
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Xu, He Long, Jun Xiao, and Yu Xin Zhang. "Dynamical Model Updating Based on Gradient Regularization Method." Applied Mechanics and Materials 351-352 (August 2013): 118–21. http://dx.doi.org/10.4028/www.scientific.net/amm.351-352.118.

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Modulus of elasticity is an important input parameter in all kinds of structural analyses. The mathematical model used to identify the structural elastic modulus with measured Frequencies and mode shapes at several points is thusly built up in this paper, and then Gradient-Regularization method, an inverse problem solution method, is employed to solve the problem. General finite element program is compiled, and numerical examples have proved that the method of this thesis is efficient. The issues such as the choice of model error and the choice of measuring points are discussed as well.
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Bahmani, Sohail, Petros T. Boufounos, and Bhiksha Raj. "Learning Model-Based Sparsity via Projected Gradient Descent." IEEE Transactions on Information Theory 62, no. 4 (April 2016): 2092–99. http://dx.doi.org/10.1109/tit.2016.2515078.

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Dissertations / Theses on the topic "Gradient-based model"

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Ganguly, Sandipan. "Algorithmic Modifications to a Multidisciplinary Design Optimization Model of Containerships." Thesis, Virginia Tech, 2002. http://hdl.handle.net/10919/32483.

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When designing a ship, a designer often begins with â an ideaâ of what the ship might look like and what specifications the ship should meet. The multidisciplinary design optimization model is a tool that combines an analysis and an optimization process and uses a measure of merit to obtain what it infers to be the best design. All that the designer has to know is the range of values of certain design variables that confine the design within a lower and an upper bound. The designer then feeds the MDO model with any arbitrary design within the bounds and the model searches for the best design that minimizes or maximizes a measure of merit and also meets a set of structural and stability requirements. The model is multidisciplinary because the analysis process, which calculates the measure of merit and other performance parameters, can be a combination of sub-processes used in various fields of engineering. The optimization process can also be a variety of mathematical programming techniques depending on the type of the design problem. The container ship design problem is a combination of discreet and continuous sub-problems. But to avail the advantages of gradient-based optimization algorithms, the design problem is molded into a fully continuous problem. The efficiency and effectiveness with which an optimization process achieves the best design depends on how well the design problem is posed for the optimizer and how well that particular optimization algorithm tackles the type of design problems posed before it. This led the author to investigate the details of the analysis and the optimization process within the MDO model and make modifications to each of the processes, so that the two become more compatible towards achieving a better final design. Modifications made within the optimization algorithm were then used to develop a generalized modification method that can be used to improve any gradient-based optimization algorithm.
Master of Science
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Käpernick, Bartosz [Verfasser]. "Gradient-based nonlinear model predictive control with constraint transformation for fast dynamical systems / Bartosz Käpernick." Ulm : Universität Ulm, 2016. http://d-nb.info/1166756491/34.

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Poole, Adam James. "Macroscopic traffic model validation of large networks and the introduction of a gradient based solver." Thesis, Durham University, 2017. http://etheses.dur.ac.uk/12430/.

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Traffic models are important for the evaluation of various Intelligent Transport Systems and the development of new traffic infrastructure. In order for this to be done accurately and with confidence the correct parameter values of the model must be identified. The focus of this thesis is the identification and confirmation of these parameters, which is model validation. Validation is performed on two different models; the first-order CTM and the second-order METANET model. The CTM is validated for two UK sites of 7.8 and 21.9 km and METANET for the same two sites using a variety of meta-heuristic algorithms. This is done using a newly developed method to allow for the optimisation method to determine the number of parameters to be used and the spatial extent of their application. This allows for the removal of expert engineering knowledge and ad-hoc decomposition of networks. This thesis also develops a methodology by use of Automatic Differentiation to allow gradient based optimisation to be used. This approach successfully validated the METANET model for the 21.9 km site and also a large network surrounding the city of Manchester of 186.9 km. This proves that gradient based optimisation can be used for the macroscopic traffic model validation problem. In fact the performance of the developed gradient method is superior to the meta-heuristics tested for the same sites. The methodology defined also allows for more data to be obtained from the model such as its Jacobian and the sensitivity of the objective function being used relative to the individual parameters. Space-Time contour plots of this newly acquired data show structures and shock waves that are not visible in the mean speed contour diagrams.
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Käpernick, Bartosz Maciej [Verfasser]. "Gradient-based nonlinear model predictive control with constraint transformation for fast dynamical systems / Bartosz Käpernick." Ulm : Universität Ulm, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-8522-4.

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Patel, Kunal. "Stiffness Gradient Scaffolds as an In Vitro Model for Stem Cell Based Cardiac Cell Therapy." University of Akron / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=akron1386725736.

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Hong, Jin Gi. "Development of process-based model and novel nanocomposite cation exchange membranes for salinity gradient power production." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53530.

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Ocean salinity is a renewable energy source that has not been recognized and could provide an opportunity to capture significant amount of clean energy when it mixes with river water. One of the processes emerging as a sustainable method for capturing energy from seawater is reverse electrodialysis (RED), which generates power via the transport of the positive and negative ions in the water through selective ion exchange membranes (IEMs). RED power generation is relatively close to commercialization, but its application is often limited by system power efficiency in natural water conditions. Although various types of salt ions exist in environmental saline water, most efforts have been focused on sodium chloride as a single ionic source in the water and the effects of other common multivalent ions (e.g., magnesium and sulfate) on power generation remain unexplored. Moreover, the commercial feasibility of RED is highly challenged by the absence of specialized RED membranes. Currently available IEMs are not optimized for RED power conversion systems, but successful operation is highly dependent on the membranes used. Major advances in manufacturing of proper IEMs will be a critical pathway to accelerate large-scale energy conversion by RED. Therefore, this study aimed at advancing our understanding of the RED power system for efficient and stable salinity gradient energy generation. Specifically, it is comprised of three parts. First, a mathematical model is developed for three different monovalent and multivalent ion combinations to determine the effect of different ionic compositions of the feed solution on the power density. Efforts are further made to optimize the RED system with respect to improving power density by investigating the sensitivity of key response parameters such as flow rate ratios and intermembrane distance ratios. Second, novel organic-inorganic nanocomposite cation exchange membranes (CEMs) are synthesized for RED application by introducing functionalized inorganic materials into an organic polymer matrix. The effect of inorganic particle filler loading within the organic polymer matrix on physico- and electrochemical performance is investigated. The results revealed that the increase of functionalized nanoparticle loading controls the effective ion transport in the membrane structure and there exists an optimum amount of nanoparticles (i.e., charged groups), which performs the best in selectively exchanging counter-ions, while excluding co-ionic species. Third, the membrane structure modification is demonstrated to enhance ion transport while maintaining large surface-charged functional groups in the polymer matrix. We have synthesized custom nanocomposite CEMs to tailor porous membrane structures of various thicknesses, aging (evaporation) time, and inorganic nanoparticle loadings. We have further tailored the membrane structure by incorporating different inorganic particle filler sizes. These engineered design approaches are found to be highly effective in obtaining desired physico- and electrochemical properties, which allowed higher ionic current flow throughout the system. Furthermore, for the first time we showed the successful application of tailor-made nanocomposite CEMs in a RED stack and achieved superb power density, which exceeds the power output obtained with the commercially available membranes. In summary, this dissertation has advanced our understanding of salinity gradient energy generation using RED technique. Specifically, computational modeling and simulation study investigates the development and optimization approaches of the RED process for practical application of RED using natural water conditions. Furthermore, the RED membranes developed in this dissertation focuses on fabrication, characterization, and optimization of cation exchange membranes. Overall, the results of this study are anticipated to benefit the future optimization of energy-capturing mechanisms in RED and provide the better pathway for the sustainable salinity gradient power generation.
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Zhang, Yi. "Strategies for Combining Tree-Based Ensemble Models." NSUWorks, 2017. http://nsuworks.nova.edu/gscis_etd/1021.

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Ensemble models have proved effective in a variety of classification tasks. These models combine the predictions of several base models to achieve higher out-of-sample classification accuracy than the base models. Base models are typically trained using different subsets of training examples and input features. Ensemble classifiers are particularly effective when their constituent base models are diverse in terms of their prediction accuracy in different regions of the feature space. This dissertation investigated methods for combining ensemble models, treating them as base models. The goal is to develop a strategy for combining ensemble classifiers that results in higher classification accuracy than the constituent ensemble models. Three of the best performing tree-based ensemble methods – random forest, extremely randomized tree, and eXtreme gradient boosting model – were used to generate a set of base models. Outputs from classifiers generated by these methods were then combined to create an ensemble classifier. This dissertation systematically investigated methods for (1) selecting a set of diverse base models, and (2) combining the selected base models. The methods were evaluated using public domain data sets which have been extensively used for benchmarking classification models. The research established that applying random forest as the final ensemble method to integrate selected base models and factor scores of multiple correspondence analysis turned out to be the best ensemble approach.
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Verma, Navin Prakash. "Viscous Dampers for Optimal Reduction in Seismic Response." Thesis, Virginia Tech, 2001. http://hdl.handle.net/10919/43716.

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To model dissipation of energy in vibrating civil structures, existence of viscous damping is commonly assumed primarily for mathematical convenience. In such a classical damper, the damping force is assumed to depend only on the velocity of deformation. Fluid viscous dampers that provide this type of damping have been manufactured to provide supplementary damping in civil and mechanical systems to enhance their performance. Some fluid dampers, however, exhibit stiffening characteristics at higher frequencies of deformation. The force deformation relationship of such dampers can be better represented by the Maxwell model of visco-elasticity. This model consists of a viscous dashpot in series with a spring, the latter element providing the stiffening characteristics. This study is concerned with the optimal utilization of such Maxwell dampers for seismic performance improvement of civil structures. The force deformation relationship of Maxwell dampers is described by a first order differential equation. Earlier studies dealing with these dampers, used an unsymmetric set of equations for combined structure and damper system. The solution of such equations for response analysis or for optimization calculation by a modal analysis approach would require the pair of the left and right eigenvectors. In this study, an auxiliary variable is introduced in the representation of a Maxwell damper to obtain symmetric equations of motion for combined structure and damper system. This eliminates the need for working with two sets of eigenvectors and their derivatives, required for optimal analysis. Since the main objective of installing these dampers is to reduce the structural response in an optimal manner, the optimization problem is defined in terms of the minimization of some response-based performance indices. To calculate the optimal parameters of dampers placed at different location in the structure, Rosen's gradient projection method is employed. For numerical illustration, a 24-story shear building is considered. Numerical results are obtained for seismic input defined by a spectral density function; however, the formulation permits direct utilization of response spectrum-based description of design earthquake. Three different performance indices -- inter story drift-based, floor acceleration-based, and base shear-based performance indices-- have been considered to calculate the numerical results. A computational scheme is presented to calculate the amount of total damping required to achieve a desired level of response reduction. The effect of ignoring the stiffening effect at higher frequencies in the Maxwell model on the optimal performance is evaluated by parametric variation of relaxation time coefficient. It is observed that the models with higher relaxation time parameter show a decreased response reducing damping effect. Thus ignoring the stiffening effect when it is, indeed, present would provide an unconservative estimation of the damping effect. The effect of brace flexibilities on different performance indices is also investigated. It is observed that flexibility in a brace reduces the effectiveness of the damper.
Master of Science
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Montebello, Claudio. "Analysis of the stress gradient effect in Fretting-Fatigue through a description based on nonlocal intensity factors." Thesis, Université Paris-Saclay (ComUE), 2015. http://www.theses.fr/2015SACLN019/document.

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Nous proposons dans ce manuscrit une nouvelle méthode pour prendre en compte l’effet du gradient en Fretting-fatigue. Les champs mécaniques présents à proximité du front de contact sont décrits à travers des facteurs d’intensité non locaux. L’objectif est d’aboutir à une description du champ de vitesse sous la forme d’une somme de termes exprimés chacun comme le produit d’un facteur d’intensité (Is, Ia, Ic), qui dépend des chargements macroscopiques appliqués à l’ensemble et d’une fonction de forme (ds, da, dc), qui est liée à la géométrie locale du contact. Cette description est obtenue à travers un processus non intrusif de post-processing des résultats obtenus avec des calculs à éléments finis. De plus, elle a été pensée pour être implémentée dans un contexte industriel. En pratique, pour chaque chargement macroscopique et pour chaque géométrie, il est possible de calculer un ensemble de facteurs d’intensité non locaux qui permettent de décrire les champs mécaniques locaux près du front de contact. Cette description non locale a l’avantage d’être (i) indépendante de la géométrie du contact employé et (ii) utilisable dans des modèles à éléments finis utilisés dans l’industrie qui sont caractérisés par des maillages plus grossiers par rapport à ceux utilisés pour étudier le fretting-fatigue dans des milieux académiques. Une étude est menée pour vérifier que les facteurs d’intensité non locaux peuvent être utilisés pour transposer les résultats expérimentaux d’une géométrie à une autre
In this manuscript a new method to describe the stress gradient effect in fretting-fatigue is proposed. It is based on the description of the mechanical fields arising close to the contact edges through nonlocal intensity factors. For this purpose, the kinetic field around the contact ends is partitioned into a summation of multiple terms, each one expressed as the product between intensity factors, Is, Ia, Ic, depending on the macroscopic loads applied to the mechanical assembly, and spatial reference fields, ds, da, dc, depending on the local geometry of the part. This description is obtained through nonintrusive post-processing of FE computation and is conceived in order to be easily implementable in the industrial context. As a matter of fact, for any given macroscopic load and geometry, a set of nonlocal intensity factors is computed that permits to characterize the mechanical fields close to the contact edges. Such nonlocal description has the advantage of being (i) geometry independent so that the nonlocal intensity factors can be used to compare laboratory test with real-scale industrial assembly, (ii) applicable to industrial FE models usually characterized by rougher meshes compared to the ones used to describe fretting-fatigue in the academic context. The procedure is applied to fretting-fatigue test data in order to verify whether the nonlocal intensity factors can be used to transpose experimental results to different contact geometries from the one in which they have been obtained
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Lebeda, Aleš. "Model soustavy motorů s pružným členem." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2012. http://www.nusl.cz/ntk/nusl-219693.

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This thesis deals with problem of experimental identification using principles of artificial intelligence and development of nonlinear models. It shows how to estimate parameters of nonlinear models and it compares different types of nonlinear models based on analytical analysis which were developed from measured data in simulation and real system motors with flexible component.
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Books on the topic "Gradient-based model"

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Francis, Elaine J. Gradient Acceptability and Linguistic Theory. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780192898944.001.0001.

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In Gradient Acceptability and Linguistic Theory, Elaine J. Francis examines a challenging problem at the intersection of theoretical linguistics and the psychology of language: the problem of interpreting gradient judgments of sentence acceptability in relation to theories of grammatical knowledge. This problem is important because acceptability judgments constitute the primary source of data on which such theories have been built, despite being susceptible to various extra-grammatical factors. Through a review of experimental and corpus-based research on a variety of syntactic phenomena and an in-depth examination of two case studies, Francis argues for two main positions. The first is that converging evidence from online comprehension tasks, elicited production tasks, and corpora of naturally occurring discourse can help determine the sources of variation in acceptability judgments and narrow down the range of plausible theoretical interpretations. The second is that the interpretation of judgment data depends crucially on one’s theoretical commitments and assumptions, especially with respect to the nature of the syntax–semantics interface and the choice of either a categorical or a gradient notion of grammaticality. The theoretical frameworks considered in this book include derivational theories (e.g. Minimalism, Principles and Parameters), constraint-based theories (e.g. Sign-Based Construction Grammar, Simpler Syntax), competition-based theories (e.g. Stochastic Optimality Theory, Decathlon Model), and usage-based approaches. While showing that acceptability judgment data are typically compatible with the assumptions of various theoretical frameworks, Francis argues that some gradient phenomena are best captured within frameworks that permit soft constraints—non-categorical grammatical constraints that encode the conventional preferences of language users.
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Evtushenko, Yury, Vladimir Zubov, and Anna Albu. Optimal control of thermal processes with phase transitions. LCC MAKS Press, 2021. http://dx.doi.org/10.29003/m2449.978-5-317-06677-2.

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The optimal control of the metal solidification process in casting is considered. Quality of the obtained detail greatly depends on how the crystallization process proceeds. It is known that to obtain a model of a good quality it is desirable that the phase interface would be as close as possible to a plane and that the speed of its motion would be close to prescribed. The proposed mathematical model of the crystallization process is based on a three dimensional two phase initial-boundary value problem of the Stefan type. The velocity of the mold in the furnace is used as the control. The control satisfying the technological requirements is determined by solving the posed optimal control problem. The optimal control problem was solved numerically using gradient optimization methods. The effective method is proposed for calculation of the cost functional gradient. It is based on the fast automatic differentiation technique and produces the exact gradient for the chosen approximation of the optimal control problem.
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Weighted graph based ordering techniques for preconditioned conjugate gradient methods. [Moffett Field, Calif.]: Research Institute for Advanced Computer Science, NASA Ames Research Center, 1994.

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Book chapters on the topic "Gradient-based model"

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Takemura, Akihiro, and Katsumi Inoue. "Gradient-Based Supported Model Computation in Vector Spaces." In Logic Programming and Nonmonotonic Reasoning, 336–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-15707-3_26.

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Lin, Jianchen, and Yanqing Yao. "Differentially Private Generative Model with Ratio-Based Gradient Clipping." In Machine Learning for Cyber Security, 535–49. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20096-0_40.

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Tian, Zhiyi, Chenhan Zhang, Lei Cui, and Shui Yu. "GSMI: A Gradient Sign Optimization Based Model Inversion Method." In Lecture Notes in Computer Science, 67–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_6.

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Pham, Duc Duy, Gurbandurdy Dovletov, Sebastian Warwas, Stefan Landgraeber, Marcus Jäger, and Josef Pauli. "Gradient-Based Expanding Spherical Appearance Models for Femoral Model Initialization in MRI." In Informatik aktuell, 43–48. Wiesbaden: Springer Fachmedien Wiesbaden, 2019. http://dx.doi.org/10.1007/978-3-658-25326-4_13.

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Xiu, C., and X. Chu. "A Gradient Model for Granular Materials Based on Thermo-micromechanics." In Springer Proceedings in Physics, 409–21. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1926-5_43.

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Huang, Deng, Yufei Wang, and Wen Chen. "RLPassGAN: Password Guessing Model Based on GAN with Policy Gradient." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 159–74. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96791-8_12.

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Wang, Jian, Xiuying Li, and Xiaobin Li. "Recursive-gradient-based Model-Free Adaptive Control with RBF Neural Network." In Lecture Notes in Electrical Engineering, 539–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6324-6_55.

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Yang, Haikuan, Hua Yang, Junxiong Wang, Kang Zhou, and Bing Cai. "RON Loss Prediction Based on Model of Light Gradient Boosting Machine." In Advances in Intelligent Systems, Computer Science and Digital Economics III, 187–99. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97057-4_17.

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Sahu, Priyabrata, and Jibendu Kumar Mantri. "Stacked Generalization Ensemble-Based Hybrid Gradient Boosted Model for Predicting Diabetes." In Advances in Engineering Research, 253–65. Dordrecht: Atlantis Press International BV, 2024. http://dx.doi.org/10.2991/978-94-6463-529-4_23.

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Frühwirth, Rudolf, and Are Strandlie. "Statistics and Numerical Methods." In Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors, 33–46. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65771-0_3.

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AbstractThe chapter gives an outline of some statistical and numerical methods that will be applied in later chapters. The first section deals with the minimization of functions. Several gradient-based methods and a popular non-gradient method are discussed. The following section discusses statistical models and the estimation of model parameters. The basics of linear and nonlinear regression models and state space models are presented, including least-squares estimation and the (extended) Kalman filter. The final section gives a brief overview of clustering and different types of clustering algorithms.
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Conference papers on the topic "Gradient-based model"

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Dunnett, Kealan, Reza Arablouei, Dimity Miller, Volkan Dedeoglu, and Raja Jurdak. "Unlearning Backdoor Attacks Through Gradient-Based Model Pruning." In 2024 54th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W), 46–54. IEEE, 2024. http://dx.doi.org/10.1109/dsn-w60302.2024.00021.

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Chi, Meiqi. "Optimization of Prediction Model Based on Gradient Boosting Decision Tree." In 2024 International Conference on Integrated Intelligence and Communication Systems (ICIICS), 1–5. IEEE, 2024. https://doi.org/10.1109/iciics63763.2024.10860220.

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Puvvula, Mihit, Pranav Kulkarni, and Anaiy Somalwar. "Novel Cash-Flow Based Underwriting Model using Gradient Boosted Decision Trees." In 2024 Artificial Intelligence for Business (AIxB), 33–38. IEEE, 2024. https://doi.org/10.1109/aixb62249.2024.00012.

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Fang, Yunan, and Zhongliang Pan. "An improved Contour and Gradient based Bionic Retinex Model for Image Enhancement." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC), 803–7. IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10913064.

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Chen, Muyi, and Daling Wang. "Uncertainty estimation for gradient boosting models based on Gaussian graph model and natural gradient." In Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), edited by Fan Zhou and Shuhong Ba. SPIE, 2023. http://dx.doi.org/10.1117/12.2675209.

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La Cava, William G., and Kourosh Danai. "Model Structure Adaptation: A Gradient-Based Approach." In ASME 2015 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/dscc2015-9658.

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A gradient-based method of symbolic adaptation is introduced for a class of continuous dynamic models. The proposed Model Structure Adaptation Method (MSAM) starts with the first-principles model of the system and adapts its structure after adjusting its individual components in symbolic form. A key contribution of this work is its introduction of the model’s parameter sensitivity as the measure of symbolic changes to the model. This measure, which is essential to defining the structural sensitivity of the model, not only accommodates algebraic evaluation of candidate models in lieu of more computationally expensive simulation-based evaluation, but also makes possible the implementation of gradient-based optimization in symbolic adaptation. The applicability of the proposed method is evaluated in application to several models which demonstrate its potential utility.
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Santucci, Valentino, and Josu Ceberio. "Model-based Gradient Search using the Plackett-Luce model." In GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion. New York, NY, USA: ACM, 2024. http://dx.doi.org/10.1145/3638530.3664063.

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Morelle, Patrick, and Alain Remouchamps. "Comparison of Gradient and Non Gradient Based Methods for Crash Optimisation." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34045.

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The BOSS Quattro system (developed by Samtech s.a.) is an open architecture allowing to run various optimisation engines including gradient based methods (SQP, GCMMA, Conlin, MDAQ, ...), DOE and Response Surfaces, and Genetic Algorithms. This system has been used and various optimisation strategies compared for the resolution of non linear optimisation problems including crash worthiness and airbags opening simulation. Thanks to BOSS Quattro open architecture, using neutral or specific drivers so to read information from models, software like MADIMO, Pam Crash, LS-Dyna, but also NASTRAN, SAMCEF and Abaqus can been used in order to model the non linear behaviour of the optimised vehicles. One or several models were used and computation distributed by BOSS parallel architecture on network of workstations. Direct parallel mode can be used, or the tasks distributed on the network through a Task Manager like for example LSF. As a first example, the optimisation of a stiffened box is performed using one (dynamic) then two models (static but elasto-plastic/dynamic). The external software used are NASTRAN and ABAQUS. A second example deals with the optimisation of a Golf crash model using MADIMO to represent a sequence of events including opening of air bags. Mixed optimisation is performed using as design variables : the time to open a given airbag (continuous) or the type of wheel configuration (pure discrete variable). Constraints are related to safety of the passenger (sternum invasion, various criteria related to accelerations).
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Wei Zhang, Jiang Zhu, and Li Fang Kong. "Gradient genetic algorithm-based performance fault diagnosis model." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6010844.

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Qun, Wang, Dai Xiuyue, and Li Tian. "Gradient model based P2P VoD content distribution policy." In 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet). IEEE, 2012. http://dx.doi.org/10.1109/cecnet.2012.6201896.

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Reports on the topic "Gradient-based model"

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Bhushan, Shanti, Greg Burgreen, Wesley Brewer, and Ian Dettwiller. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes. Engineer Research and Development Center (U.S.), April 2025. https://doi.org/10.21079/11681/49702.

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A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the machine-learned model from over training on more prevalent flow regimes, and results in a model with similar accuracy. Turbulence production is correlated with shear strain in the free-shear region, with shear strain and wall-distance and local velocity-based Reynolds number in the boundary layer regime, and with streamwise velocity gradient in the accelerating flow regime. The flow direction is key in identifying flow separation and reattachment regime. Machine-learned models perform poorly in extrapolation mode. A priori tests reveal model predictability improves as the hill dataset is partially added during training in a partial extrapolation model. These also provide better turbulent kinetic energy and shear stress predictions than RANS in a posteriori tests. Before a machine-learned model is applied for a posteriori tests, a priori tests should be performed.
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Pasupuleti, Murali Krishna. Phase Transitions in High-Dimensional Learning: Understanding the Scaling Limits of Efficient Algorithms. National Education Services, March 2025. https://doi.org/10.62311/nesx/rr1125.

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Abstract: High-dimensional learning models exhibit phase transitions, where small changes in model complexity, data size, or optimization dynamics lead to abrupt shifts in generalization, efficiency, and computational feasibility. Understanding these transitions is crucial for scaling modern machine learning algorithms and identifying critical thresholds in optimization and generalization performance. This research explores the role of high-dimensional probability, random matrix theory, and statistical physics in analyzing phase transitions in neural networks, kernel methods, and convex vs. non-convex optimization. Key focus areas include the computational-to-statistical gap, double descent phenomena, and spectral phase transitions that impact model efficiency. The study also investigates the scaling limits of iterative optimization methods, highlighting when gradient-based learning succeeds or fails in high-dimensional regimes. By integrating theoretical analysis and empirical validation, this report provides a structured framework for designing scalable, efficient, and robust AI systems that can adapt to phase transitions and scaling laws in high-dimensional learning. Keywords: Phase transitions in learning, high-dimensional probability, scaling laws, statistical physics in AI, random matrix theory, computational-to-statistical gap, neural network overparameterization, double descent phenomenon, convex vs. non-convex optimization, spectral phase transitions, kernel methods in high dimensions, scaling limits in deep learning, gradient-based optimization, iterative learning algorithms, eigenvalue distributions in machine learning, large-scale AI efficiency, threshold effects in generalization, scaling-aware machine learning, AI robustness in high dimensions.
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Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, September 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

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A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thesis. Creating a melting snow albedo function and the construction of an aerosol optical depth time series lean on a linear regression approach, whereas the process to fill missing values is based on gradient boosting, a machine learning method that is in turn based on decision trees. These methods reflect the basic nature of these problems as well as the need to take into account the large amounts of satellite-based data and computational resources available.
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Oliynyk, Kateryna, and Matteo Ciantia. Application of a finite deformation multiplicative plasticity model with non-local hardening to the simulation of CPTu tests in a structured soil. University of Dundee, December 2021. http://dx.doi.org/10.20933/100001230.

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In this paper an isotropic hardening elastoplastic constitutive model for structured soils is applied to the simulation of a standard CPTu test in a saturated soft structured clay. To allow for the extreme deformations experienced by the soil during the penetration process, the model is formulated in a fully geometric non-linear setting, based on: i) the multiplicative decomposition of the deformation gradient into an elastic and a plastic part; and, ii) on the existence of a free energy function to define the elastic behaviour of the soil. The model is equipped with two bonding-related internal variables which provide a macroscopic description of the effects of clay structure. Suitable hardening laws are employed to describe the structure degradation associated to plastic deformations. The strain-softening associated to bond degradation usually leads to strain localization and consequent formation of shear bands, whose thickness is dependent on the characteristics of the microstructure (e.g, the average grain size). Standard local constitutive models are incapable of correctly capturing this phenomenon due to the lack of an internal length scale. To overcome this limitation, the model is framed using a non-local approach by adopting volume averaged values for the internal state variables. The size of the neighbourhood over which the averaging is performed (characteristic length) is a material constant related to the microstructure which controls the shear band thickness. This extension of the model has proven effective in regularizing the pathological mesh dependence of classical finite element solutions in the post-localization regime. The results of numerical simulations, conducted for different soil permeabilities and bond strengths, show that the model captures the development of plastic deformations induced by the advancement of the cone tip; the destructuration of the clay associated with such plastic deformations; the space and time evolution of pore water pressure as the cone tip advances. The possibility of modelling the CPTu tests in a rational and computationally efficient way opens a promising new perspective for their interpretation in geotechnical site investigations.
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Pasupuleti, Murali Krishna. Stochastic Computation for AI: Bayesian Inference, Uncertainty, and Optimization. National Education Services, March 2025. https://doi.org/10.62311/nesx/rriv325.

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Abstract: Stochastic computation is a fundamental approach in artificial intelligence (AI) that enables probabilistic reasoning, uncertainty quantification, and robust decision-making in complex environments. This research explores the theoretical foundations, computational techniques, and real-world applications of stochastic methods, focusing on Bayesian inference, Monte Carlo methods, stochastic optimization, and uncertainty-aware AI models. Key topics include probabilistic graphical models, Markov Chain Monte Carlo (MCMC), variational inference, stochastic gradient descent (SGD), and Bayesian deep learning. These techniques enhance AI's ability to handle uncertain, noisy, and high-dimensional data while ensuring scalability, interpretability, and trustworthiness in applications such as robotics, financial modeling, autonomous systems, and healthcare AI. Case studies demonstrate how stochastic computation improves self-driving car navigation, financial risk assessment, personalized medicine, and reinforcement learning-based automation. The findings underscore the importance of integrating probabilistic modeling with deep learning, reinforcement learning, and optimization techniques to develop AI systems that are more adaptable, scalable, and uncertainty-aware. Keywords Stochastic computation, Bayesian inference, probabilistic AI, Monte Carlo methods, Markov Chain Monte Carlo (MCMC), variational inference, uncertainty quantification, stochastic optimization, Bayesian deep learning, reinforcement learning, probabilistic graphical models, stochastic gradient descent (SGD), uncertainty-aware AI, probabilistic reasoning, risk assessment, AI in robotics, AI in finance, AI in healthcare, decision-making under uncertainty, trustworthiness in AI, scalable AI, interpretable AI.
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Liu, Hongrui, and Rahul Ramachandra Shetty. Analytical Models for Traffic Congestion and Accident Analysis. Mineta Transportation Institute, November 2021. http://dx.doi.org/10.31979/mti.2021.2102.

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In the US, over 38,000 people die in road crashes each year, and 2.35 million are injured or disabled, according to the statistics report from the Association for Safe International Road Travel (ASIRT) in 2020. In addition, traffic congestion keeping Americans stuck on the road wastes millions of hours and billions of dollars each year. Using statistical techniques and machine learning algorithms, this research developed accurate predictive models for traffic congestion and road accidents to increase understanding of the complex causes of these challenging issues. The research used US Accidents data consisting of 49 variables describing 4.2 million accident records from February 2016 to December 2020, as well as logistic regression, tree-based techniques such as Decision Tree Classifier and Random Forest Classifier (RF), and Extreme Gradient boosting (XG-boost) to process and train the models. These models will assist people in making smart real-time transportation decisions to improve mobility and reduce accidents.
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Epel, Bernard, and Roger Beachy. Mechanisms of intra- and intercellular targeting and movement of tobacco mosaic virus. United States Department of Agriculture, November 2005. http://dx.doi.org/10.32747/2005.7695874.bard.

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To cause disease, plant viruses must replicate and spread locally and systemically within the host. Cell-to-cell virus spread is mediated by virus-encoded movement proteins (MPs), which modify the structure and function of plasmodesmata (Pd), trans-wall co-axial membranous tunnels that interconnect the cytoplasm of neighboring cells. Tobacco mosaic virus (TMV) employ a single MP for cell- cell spread and for which CP is not required. The PIs, Beachy (USA) and Epel (Israel) and co-workers, developed new tools and approaches for study of the mechanism of spread of TMV that lead to a partial identification and molecular characterization of the cellular machinery involved in the trafficking process. Original research objectives: Based on our data and those of others, we proposed a working model of plant viral spread. Our model stated that MPᵀᴹⱽ, an integral ER membrane protein with its C-terminus exposed to the cytoplasm (Reichel and Beachy, 1998), alters the Pd SEL, causes the Pd cytoplasmic annulus to dilate (Wolf et al., 1989), allowing ER to glide through Pd and that this gliding is cytoskeleton mediated. The model claimed that in absence of MP, the ER in Pd (the desmotubule) is stationary, i.e. does not move through the Pd. Based on this model we designed a series of experiments to test the following questions: -Does MP potentiate ER movement through the Pd? - In the presence of MP, is there communication between adjacent cells via ER lumen? -Does MP potentiate the movement of cytoskeletal elements cell to cell? -Is MP required for cell-to-cell movement of ER membranes between cells in sink tissue? -Is the binding in situ of MP to RNA specific to vRNA sequences or is it nonspecific as measured in vitro? And if specific: -What sequences of RNA are involved in binding to MP? And finally, what host proteins are associated with MP during intracellular targeting to various subcellular targets and what if any post-translational modifications occur to MP, other than phosphorylation (Kawakami et al., 1999)? Major conclusions, solutions and achievements. A new quantitative tool was developed to measure the "coefficient of conductivity" of Pd to cytoplasmic soluble proteins. Employing this tool, we measured changes in Pd conductivity in epidermal cells of sink and source leaves of wild-type and transgenic Nicotiana benthamiana (N. benthamiana) plants expressing MPᵀᴹⱽ incubated both in dark and light and at 16 and 25 ᵒC (Liarzi and Epel, 2005 (appendix 1). To test our model we measured the effect of the presence of MP on cell-to-cell spread of a cytoplasmic fluorescent probe, of two ER intrinsic membrane protein-probes and two ER lumen protein-probes fused to GFP. The effect of a mutant virus that is incapable of cell-to-cell spread on the spread of these probes was also determined. Our data shows that MP reduces SEL for cytoplasmic molecules, dilates the desmotubule allowing cell-cell diffusion of proteins via the desmotubule lumen and reduces the rate of spread of the ER membrane probes. Replicase was shown to enhance cell-cell spread. The data are not in support of the proposed model and have led us to propose a new model for virus cell-cell spread: this model proposes that MP, an integral ER membrane protein, forms a MP:vRNAER complex and that this ER-membrane complex diffuses in the lipid milieu of the ER into the desmotubule (the ER within the Pd), and spreads cell to cell by simple diffusion in the ER/desmotubule membrane; the driving force for spread is the chemical potential gradient between an infected cell and contingent non-infected neighbors. Our data also suggests that the virus replicase has a function in altering the Pd conductivity. Transgenic plant lines that express the MP gene of the Cg tobamovirus fused to YFP under the control the ecdysone receptor and methoxyfenocide ligand were generated by the Beachy group and the expression pattern and the timing and targeting patterns were determined. A vector expressing this MPs was also developed for use by the Epel lab . The transgenic lines are being used to identify and isolate host genes that are required for cell-to-cell movement of TMV/tobamoviruses. This line is now being grown and to be employed in proteomic studies which will commence November 2005. T-DNA insertion mutagenesis is being developed to identify and isolate host genes required for cell-to-cell movement of TMV.
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Meir, Shimon, Michael Reid, Cai-Zhong Jiang, Amnon Lers, and Sonia Philosoph-Hadas. Molecular Studies of Postharvest Leaf and Flower Abscission. United States Department of Agriculture, 2005. http://dx.doi.org/10.32747/2005.7696523.bard.

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Original objectives: Understanding the regulation of abscission competence by exploring the nature and function of auxin-related gene expression changes in the leaf and pedicelAZs of tomato (as a model system), was the main goal of the previously submitted proposal. We proposed to achieve this goal by using microarray GeneChip analysis, to identify potential target genes for functional analysis by virus-induced gene silencing (VIGS). To increase the potential of accomplishing the objectives of the previously submitted proposal, we were asked by BARD to show feasibility for the use of these two modern techniques in our abscission system. Thus, the following new objectives were outlined for the one-year feasibility study: 1.to demonstrate the feasibility of the VIGS system in tomato to perform functional analysis of known abscission-related genes; 2. to demonstrate that by using microarray analysis we can identify target genes for further VIGS functional analysis. Background to the topic: It is a generally accepted model that auxin flux through the abscission zone (AZ) prevents organ abscission by rendering the AZ insensitive to ethylene. However, the molecular mechanisms responsible for acquisition of abscission competence and the way in which the auxin gradient modulates it are still unknown. Understanding this basic stage of the abscission process may provide us with future tools to control abscission for agricultural applications. Based on our previous study, performed to investigate the molecular changes occurring in leaf and stem AZs of MirabillisJalapaL., we have expanded our research to tomato, using genomic approaches that include modern techniques for gene discovery and functional gene characterization. In our one-year feasibility study, the US team has established a useful system for VIGS in tomato, using vectors based on the tobacco rattle virus (TRV), a Lcreporter gene for silencing (involved in regulation of anthocyanin biosynthesis), and the gene of interest. In parallel, the Israeli team has used the newly released Affymetrix Tomato GeneChip to measure gene expression in AZ and non-AZ tissues at various time points after flower removal, when increased sensitivity to ethylene is acquired prior to abscission (at 0-8 h), and during pedicelabscission (at 14 h). In addition, gene expression was measured in the pedicel AZ pretreated with the ethylene action inhibitor, 1-methylcyclopropene (1-MCP) before flower removal, to block any direct effects of ethylene. Major conclusions, solutions and achievements: 1) The feasibility study unequivocally established that VIGS is an ideal tool for testing the function of genes with putative roles in abscission; 2) The newly released Affymetrix Tomato GeneChip was found to be an excellent tool to identify AZ genes possibly involved in regulation and execution of abscission. The VIGS-based study allowed us to show that TAPG, a polygalacturonase specifically associated with the tomato AZ, is a key enzyme in the abscission process. Using the newly released Affymetrix Tomato GeneChip we have identified potential abscission regulatory genes as well as new AZ-specific genes, the expression of which was modified after flower removal. These include: members of the Aux/IAAgene family, ethylene signal transduction-related genes, early and late expressed transcription factors, genes which encode post-translational regulators whose expression was modified specifically in the AZ, and many additional novel AZ-specific genes which were previously not associated with abscission. This microarray analysis allowed us to select an initial set of target genes for further functional analysis by VIGS. Implications: Our success in achieving the two objectives of this feasibility study provides us with a solid basis for further research outlined in the original proposal. This will significantly increase the probability of success of a full 3-year project. Additionally, our feasibility study yielded highly innovative results, as they represent the first direct demonstration of the functional involvement of a TAPG in abscission, and the first microarray analysis of the abscission process. Using these approaches we could identify a large number of genes involved in abscission regulation, initiation and execution, and in auxin-ethylene cross-talk, which are of great importance, and could enable their potential functional analysis by VIGS.
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Gu, Tianbao, Samuel Simon Araya, and Vincenzo Liso. CFD Modeling of NH3 Synthesis on Ru-Based Catalyst for Hydrogen Storage and Transport. Aalborg University, 2024. https://doi.org/10.54337/aau772652211.

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Hydrogen is often considered as a promising alternative to fossil fuels, however its low boiling point and diffusivity cause a huge challenge for storage and transport. Therefore an alternative fuel like ammonia, containing 17 wt.% hydrogen with a higher boiling point, seems a very promising solution for hydrogen storage and transport. In this paper, ammonia synthesis via the most widely used Haber-Bosch process has been investigated. A computational fluid dynamics (CFD) model is developed for a lab-scale reactor, incorporating the modified Temkin kinetic model describing the complex catalytic reaction on the novel Ruthenium (Ru) catalyst. The isothermal simulation is first carried out for validation, i.e., comparing with the experimental data performed under the same conditions. Then the non-isothermal simulation is conducted to investigate the synthesis process in detail for the case study. The temperature gradient and species distributions inside the catalyst bed are captured and analyzed, which shows meaningful references for the design and operation of ammonia synthesis demonstrators.
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Russo, David, and William A. Jury. Characterization of Preferential Flow in Spatially Variable Unsaturated Field Soils. United States Department of Agriculture, October 2001. http://dx.doi.org/10.32747/2001.7580681.bard.

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Preferential flow appears to be the rule rather than the exception in field soils and should be considered in the quantitative description of solute transport in the unsaturated zone of heterogeneous formations on the field scale. This study focused on both experimental monitoring and computer simulations to identify important features of preferential flow in the natural environment. The specific objectives of this research were: (1) To conduct dye tracing and multiple tracer experiments on undisturbed field plots to reveal information about the flow velocity, spatial prevalence, and time evolution of a preferential flow event; (2) To conduct numerical experiments to determine (i) whether preferential flow observations are consistent with the Richards flow equation; and (ii) whether volume averaging over a domain experiencing preferential flow is possible; (3) To develop a stochastic or a transfer function model that incorporates preferential flow. Regarding our field work, we succeeded to develop a new method for detecting flow patterns faithfully representing the movement of water flow paths in structured and non-structured soils. The method which is based on application of ammonium carbonate was tested in a laboratory study. Its use to detect preferential flow was also illustrated in a field experiment. It was shown that ammonium carbonate is a more conservative tracer of the water front than the popular Brilliant Blue. In our detailed field experiments we also succeeded to document the occurrence of preferential flow during soil water redistribution following the cessation of precipitation in several structureless field soils. Symptoms of the unstable flow observed included vertical fingers 20 - 60 cm wide, isolated patches, and highly concentrated areas of the tracers in the transmission zone. Soil moisture and tracer measurements revealed that the redistribution flow became fingered following a reversal of matric potential gradient within the wetted area. Regarding our simulation work, we succeeded to develop, implement and test a finite- difference, numerical scheme for solving the equations governing flow and transport in three-dimensional, heterogeneous, bimodal, flow domains with highly contrasting soil materials. Results of our simulations demonstrated that under steady-state flow conditions, the embedded clay lenses (with very low conductivity) in bimodal formations may induce preferential flow, and, consequently, may enhance considerably both the solute spreading and the skewing of the solute breakthrough curves. On the other hand, under transient flow conditions associated with substantial redistribution periods with diminishing water saturation, the effect of the embedded clay lenses on the flow and the transport might diminish substantially. Regarding our stochastic modeling effort, we succeeded to develop a theoretical framework for flow and transport in bimodal, heterogeneous, unsaturated formations, based on a stochastic continuum presentation of the flow and a general Lagrangian description of the transport. Results of our analysis show that, generally, a bimodal distribution of the formation properties, characterized by a relatively complex spatial correlation structure, contributes to the variability in water velocity and, consequently, may considerably enhance solute spreading. This applies especially in formations in which: (i) the correlation length scales and the variances of the soil properties associated with the embedded soil are much larger than those of the background soil; (ii) the contrast between mean properties of the two subdomains is large; (iii) mean water saturation is relatively small; and (iv) the volume fraction of the flow domain occupied by the embedded soil is relatively large.
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