Relevant bibliographies by topics / Surrogate dynamics

Contents

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

Academic literature on the topic 'Surrogate dynamics'

Author: Grafiati
Published: 30 November 2024
Last updated: 31 July 2025

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Journal articles on the topic "Surrogate dynamics"

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Varsha D. Vyas. "Investigating the Commercial Surrogacy Sector in Mumbai: Trends, Challenges, and Dynamics." Journal of Information Systems Engineering and Management 10, no. 42s (2025): 1124–37. https://doi.org/10.52783/jisem.v10i42s.8265.

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The commercial surrogacy sector in Mumbai has emerged as a complex and controversial phenomenon within the realm of assisted reproductive technologies (ART), driven by rising infertility rates, changing societal norms, and the increasing acceptance of alternative family structures. Despite India's relatively low medical costs and advanced healthcare infrastructure, the industry faces significant challenges, including a lack of comprehensive legal protections for surrogate mothers and intended parents. Ethical dilemmas arise as many surrogate mothers enter agreements out of financial necessity,
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Huang, C.-K., Q. Tang, Y. K. Batygin, et al. "Symplectic neural surrogate models for beam dynamics." Journal of Physics: Conference Series 2687, no. 6 (2024): 062026. http://dx.doi.org/10.1088/1742-6596/2687/6/062026.

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Abstract Development of robust machine-learning (ML) based surrogates for particle accelerators can significantly benefit the modeling, design, optimization, monitoring and control of such accelerators. It is desirable that the surrogate models embed fundamental physical constraints to the interaction and dynamics of the beams, for which an accelerator must be designed to operate upon. We implement and train a class of phase space structure-preserving neural networks — Henon Neural Networks (HenonNets) [1], for nonlinear beam dynamics problems. It is demonstrated that the trained HenonNet mode
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NAKAMURA, TOMOMICHI, and MICHAEL SMALL. "APPLYING THE METHOD OF SMALL–SHUFFLE SURROGATE DATA: TESTING FOR DYNAMICS IN FLUCTUATING DATA WITH TRENDS." International Journal of Bifurcation and Chaos 16, no. 12 (2006): 3581–603. http://dx.doi.org/10.1142/s0218127406016999.

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Recently, a new surrogate method, the Small–Shuffle (SS) surrogate method, has been proposed to investigate whether there is some kind of dynamics in irregular fluctuations, even if they are modulated by long term trends or periodicities. This situation is theoretically incompatible with the assumption underlying previously proposed surrogate methods. We apply the SS surrogate method to a variety of simulated data with known dynamics and actual time series with unknown dynamics.
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Koutsoupakis, Josef, and Dimitrios Giagopoulos. "Drivetrain Response Prediction Using AI-based Surrogate and Multibody Dynamics Model." Machines 11, no. 5 (2023): 514. http://dx.doi.org/10.3390/machines11050514.

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Numerical models, such as multibody dynamics ones, are broadly used in various engineering applications, either as an integral part of the preliminary design of a product or simply to analyze its behavior. Aiming to increase the accuracy and potential of these models, complex mechanisms are constantly being added to existing methods of simulation, leading to powerful modelling frameworks that are able to simulate most mechanical systems. This increase in accuracy and flexibility, however, comes at a great computational cost. To mitigate the issue of high computation times, surrogates, such as
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Charles, Giovanni, Timothy M. Wolock, Peter Winskill, Azra Ghani, Samir Bhatt, and Seth Flaxman. "Seq2Seq Surrogates of Epidemic Models to Facilitate Bayesian Inference." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (2023): 14170–77. http://dx.doi.org/10.1609/aaai.v37i12.26658.

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Epidemic models are powerful tools in understanding infectious disease. However, as they increase in size and complexity, they can quickly become computationally intractable. Recent progress in modelling methodology has shown that surrogate models can be used to emulate complex epidemic models with a high-dimensional parameter space. We show that deep sequence-to-sequence (seq2seq) models can serve as accurate surrogates for complex epidemic models with sequence based model parameters, effectively replicating seasonal and long-term transmission dynamics. Once trained, our surrogate can predict
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Xu, Lin, Hongyu Nie, Xiangyang Cheng, Qi Wei, Hongyu Chen, and Jianfeng Tao. "Surrogate Model of Hydraulic Actuator for Active Motion Compensation Hydraulic Crane." Electronics 14, no. 13 (2025): 2678. https://doi.org/10.3390/electronics14132678.

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Offshore cranes equipped with active motion compensation (AMC) systems play a vital role in marine engineering tasks such as offshore wind turbine maintenance, subsea operations, and dynamic load positioning under wave-induced disturbances. These systems rely on complex hydraulic actuators whose strongly nonlinear dynamics—often described by differential-algebraic equations (DAEs)—impose significant computational burdens, particularly in real-time applications like hardware-in-the-loop (HIL) simulation, digital twins, and model predictive control. To address this bottleneck, we propose a neura
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Chen, Menghui, Xiaoshu Gao, Cheng Chen, Tong Guo, and Weijie Xu. "A Comparative Study of Meta-Modeling for Response Estimation of Stochastic Nonlinear MDOF Systems Using MIMO-NARX Models." Applied Sciences 12, no. 22 (2022): 11553. http://dx.doi.org/10.3390/app122211553.

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Complex dynamic behavior of nonlinear structures makes it challenging for uncertainty analysis through Monte Carlo simulations (MCS). Surrogate modeling presents an efficient and accurate computational alternative for a large number of MCS. The previous study has demonstrated that the multi-input multi-output nonlinear autoregressive with exogenous input (MIMO-NARX) model provides good discrete-time representations of deterministic nonlinear multi-degree-of-freedom (MDOF) structural dynamic systems. Model order reduction (MOR) is executed to eliminate insignificant modes to reduce the computat
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Liu, Shizhong, Ziyao Wang, Jingwen Chen, Rui Xu, and Dong Ming. "The Estimation of Knee Medial Force with Substitution Parameters during Walking and Turning." Sensors 24, no. 17 (2024): 5595. http://dx.doi.org/10.3390/s24175595.

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Purpose: Knee adduction, flexion moment, and adduction angle are often used as surrogate parameters of knee medial force. To verify whether these parameters are suitable as surrogates under different walking states, we investigated the correlation between knee medial loading with the surrogates during walking and turning. Methods: Sixteen healthy subjects were recruited to complete straight walk (SW), step turn (ST), and crossover turn (CT). Knee joint moments were obtained using inverse dynamics, and knee medial force was computed using a previously validated musculoskeletal model, Freebody.
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Gong, Xu, Zhengqi Gu, and Zhenlei Li. "Surrogate model for aerodynamic shape optimization of a tractor-trailer in crosswinds." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 226, no. 10 (2012): 1325–39. http://dx.doi.org/10.1177/0954407012442295.

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A surrogate model-based aerodynamic shape optimization method applied to the wind deflector of a tractor-trailer is presented in this paper. The aerodynamic drag coefficient of the tractor-trailer with and without the wind deflector subjected to crosswinds is analyzed. The numerical results show that the wind deflector can decrease drag coefficient. Four parameters are used to describe the wind deflector geometry: width, length, height, and angle. A 30-level design of experiments study using the optimal Latin hypercube method was conducted to analyze the sensitivity of the design variables and
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Feng, Yang, Chunfa Zhao, Xin Liang, and Zhan Bai. "SNN-Based Surrogate Modeling of Electromagnetic Force and Its Application in Maglev Vehicle Dynamics Simulation." Actuators 14, no. 3 (2025): 112. https://doi.org/10.3390/act14030112.

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The majority of electromagnetic force calculation models employed in maglev vehicle system dynamics focus exclusively on vertical and lateral movement while neglecting the nonlinear magnetization properties of ferromagnetic materials. This oversight leads to discrepancies between the dynamics simulations and actual conditions. To enhance the accuracy of dynamics simulations and evaluate the performance of maglev vehicle systems under various operational conditions, it is imperative to identify an electromagnetic force calculation model that combines accuracy and applicability. To address this
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Dissertations / Theses on the topic "Surrogate dynamics"

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Koch, Christiane. "Quantum dissipative dynamics with a surrogate Hamiltonian." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät I, 2002. http://dx.doi.org/10.18452/14816.

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Diese Dissertation untersucht Quantensysteme in kondensierter Phase, welche mit ihrer Umgebung wechselwirken und durch ultrakurze Laserpulse angeregt werden. Die Zeitskalen der verschiedenen beteiligten Prozessen lassen sich bei solchen Problemen nicht separieren, weshalb die Standardmethoden zur Behandlung offener Quantensysteme nicht angewandt werden können. Die Methode des Surrogate Hamiltonian stellt ein Beispiel neuer Herangehensweisen an dissipative Quantendynamik dar. Die Weiterentwicklung der Methode und ihre Anwendung auf Phänomene, die zur Zeit experimentell untersucht werden, stehen
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Hibbs, Ryan E. "Conformational dynamics of the acetylcholine binding protein, a Nicotinic receptor surrogate." Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2006. http://wwwlib.umi.com/cr/ucsd/fullcit?p3237010.

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Thesis (Ph. D.)--University of California, San Diego, 2006.<br>Title from first page of PDF file (viewed December 8, 2006). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references.
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Conradie, Tanja. "Modelling of nonlinear dynamic systems : using surrogate data methods." Thesis, Stellenbosch : Stellenbosch University, 2000. http://hdl.handle.net/10019.1/51834.

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Thesis (MSc)--Stellenbosch University, 2000.<br>ENGLISH ABSTRACT: This study examined nonlinear modelling techniques as applied to dynamic systems, paying specific attention to the Method of Surrogate Data and its possibilities. Within the field of nonlinear modelling, we examined the following areas of study: attractor reconstruction, general model building techniques, cost functions, description length, and a specific modelling methodology. The Method of Surrogate Data was initially applied in a more conventional application, i.e. testing a time series for nonlinear, dynamic structure.
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Millard, Daniel C. "Identification and control of neural circuit dynamics for natural and surrogate inputs in-vivo." Diss., Georgia Institute of Technology, 2014. http://hdl.handle.net/1853/53405.

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A principal goal of neural engineering is to control the activation of neural circuits across space and time. The ability to control neural circuits with surrogate inputs is needed for the development of clinical neural prostheses and the experimental interrogation of connectivity between brain regions. Electrical stimulation provides a clinically viable method for activating neural tissue and the emergence of optogenetic stimulation has redefined the limitations on stimulating neural tissue experimentally. However, it remains poorly understood how these tools activate complex neural circui
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Segee, Molly Catherine. "Surrogate Models for Transonic Aerodynamics for Multidisciplinary Design Optimization." Thesis, Virginia Tech, 2016. http://hdl.handle.net/10919/71321.

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Multidisciplinary design optimization (MDO) requires many designs to be evaluated while searching for an optimum. As a result, the calculations done to evaluate the designs must be quick and simple to have a reasonable turn-around time. This makes aerodynamic calculations in the transonic regime difficult. Running computational fluid dynamics (CFD) calculations within the MDO code would be too computationally expensive. Instead, CFD is used outside the MDO to find two-dimensional aerodynamic properties of a chosen airfoil shape, BACJ, at a number of points over a range of thickness-to-chord ra
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Minsavage, Kaitlyn Emily. "Neural Networks as Surrogates for Computational Fluid Dynamics Predictions of Hypersonic Flows." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1610017352981371.

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Lagerstrom, Tiffany. "All in the Family: The Role of Sibling Relationships as Surrogate Attachment Figures." Scholarship @ Claremont, 2018. http://scholarship.claremont.edu/scripps_theses/1138.

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While several studies have analyzed the impact of mother-child attachment security on the child’s emotion regulation abilities, few studies have proposed interventions to help children improve emotion regulation abilities in the presence of an insecure mother-child attachment. This current study extends previous findings about the influence of mother-child attachment on the child’s emotion regulation abilities and contributes new research in determining whether an older sibling can moderate this effect. This study predicts that across points of assessments: 18 months, 5 years, 10 years, and 15
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Brouwer, Kirk Rowse. "Enhancement of CFD Surrogate Approaches for Thermo-Structural Response Prediction in High-Speed Flows." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1543340520905498.

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Sadet, Jérémy. "Surrogate models for the analysis of friction induced vibrations under uncertainty." Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2022. http://www.theses.fr/2022UPHF0014.

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Le crissement automobile est une nuisance sonore qui intéresse à la fois les chercheurs et les industriels. Ce phénomène fugace, perçu par les acquéreurs de véhicule comme gage de piètre qualité, induit un coût de plus en plus important pour les équipementiers automobiles dû aux réclamations-client. Par conséquent, il est primordial de proposer et développer des méthodes permettant de prédire avec efficacité l’occurrence de cette nuisance sonore grâce à des modèles de simulation numérique. Ainsi, cette thèse se propose de poursuivre les récents travaux montrant l’apport certain d’une intégrati
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Taheri, Mehdi. "Machine Learning from Computer Simulations with Applications in Rail Vehicle Dynamics and System Identification." Diss., Virginia Tech, 2016. http://hdl.handle.net/10919/81417.

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The application of stochastic modeling for learning the behavior of multibody dynamics models is investigated. The stochastic modeling technique is also known as Kriging or random function approach. Post-processing data from a simulation run is used to train the stochastic model that estimates the relationship between model inputs, such as the suspension relative displacement and velocity, and the output, for example, sum of suspension forces. Computational efficiency of Multibody Dynamics (MBD) models can be improved by replacing their computationally-intensive subsystems with stochastic pred
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Books on the topic "Surrogate dynamics"

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T, Patera Anthony, and Langley Research Center, eds. Surrogates for numerical simulations, optimization of eddy-promoter heat exchanges. National Aeronautics and Space Administration, Langley Research Center, 1993.

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Riches, D. Analysis and evaluation of different types of test surrogate employed in the dynamic performance testing of fall-arrest equipment. HSE Books, 2002.

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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Entropy and Surrogate Testing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0005.

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Reconstructing real-world system dynamics from time series data on a single variable is challenging because real-world data often exhibit a highly volatile and irregular appearance potentially driven by several diverse factors. NLTS methods help eliminate less likely drivers of dynamic irregularity. We set a benchmark for regular behavior by investigating how linear systems of ODEs are restricted to exponential and periodic dynamics, and illustrating how irregular behavior can arise if regular linear dynamics are corrupted with noise or shift over time (i.e., nonstationarity). We investigate h
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Majumdar, Anindita. Transnational Commercial Surrogacy and the (Un)Making of Kin in India. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199474363.001.0001.

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Billed as an emerging transnational industry, the commercial surrogacy arrangement is more than mere commerce. It involves the birth of kin and relationships that include cross-cultural dialogues and conflicts between forms of reproduction and birthing. The process of making kin is fraught with different forms of negotiations regarding biology, nurture, pregnancy, and parenthood. This book engages with the idea of emerging forms of families and meanings of kinship in a transnational world through ethnographic research, kinship, gender studies, and science and technology studies. The ethnograph
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Huffaker, Ray, Marco Bittelli, and Rodolfo Rosa. Data Preprocessing. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198782933.003.0006.

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Successful reconstruction of a shadow attractor provides preliminary empirical evidence that a signal isolated from observed time series data may be generated by deterministic dynamics. However, because we cannot reasonably expect signal processing to purge the signal of all noise in practice, and because noisy linear behavior can be visually indistinguishable from nonlinear behavior, the possibility remains that noticeable regularity detected in a shadow attractor may be fortuitously reconstructed from data generated by a linear-stochastic process. This chapter investigates how we can test th
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McAuley, Danny F., and Thelma Rose Craig. Measurement of extravascular lung water in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0140.

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The accumulation of fluid in the interstitium and alveolar space is known as extravascular lung water (EVLW). EVLW is associated with increased morbidity and mortality in critically ill patients and is elevated in patients with cardiogenic pulmonary oedema, acute lung injury (ALI), and the acute respiratory distress syndrome (ARDS). Pulmonary oedema is a consequence of increased pulmonary capillary hydrostatic pressure and/or an increased capillary permeability. The quantity of pulmonary oedema fluid is dependent on the balance of fluid formation and clearance, and this contributes to the over
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Book chapters on the topic "Surrogate dynamics"

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Husain, Afzal, and Kwang-Yong Kim. "Optimization of Ribbed Microchannel Heat Sink Using Surrogate Analysis." In Computational Fluid Dynamics 2008. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01273-0_69.

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Paripovic, Jelena, and Patricia Davies. "Characterizing the Dynamics of Systems Incorporating Surrogate Energetic Materials." In Special Topics in Structural Dynamics, Volume 6. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29910-5_10.

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Chiambaretto, Pierre-Louis, Miguel Charlotte, Joseph Morlier, Philippe Villedieu, and Yves Gourinat. "Surrogate Granular Materials for Modal Test of Fluid Filled Tanks." In Special Topics in Structural Dynamics, Volume 6. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29910-5_14.

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Taflanidis, Alexandros A., Jize Zhang, and Dimitris Patsialis. "Applications of Reduced Order and Surrogate Modeling in Structural Dynamics." In Model Validation and Uncertainty Quantification, Volume 3. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-12075-7_35.

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Kůdela, Jakub, and Ladislav Dobrovský. "Performance Comparison of Surrogate-Assisted Evolutionary Algorithms on Computational Fluid Dynamics Problems." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-70068-2_19.

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Papacharalampopoulos, Alexios, Christos Papaioannou, Olga Maria Karagianni, and Panagiotis Stavropoulos. "Towards Explicable AI in Systemic Identification of Surrogate Models of Manufacturing Processes." In Lecture Notes in Mechanical Engineering. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-86489-6_7.

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Abstract Surrogate models of manufacturing processes are highly useful in the context of digital twins, as they can be considered as a prerequisite for linking between physics and real machines’ cases. However, in many cases of surrogate models, the so-called (hyper)parameters are not easy to be estimated. In this work, the role of AI is investigated in terms of its efficiency in doing that in an (semi) automated way. The case of ARX models is considered for this, where the parameters are clearly related to the physics of the process. In particular, the technique of systemic identification is
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Sipponen, P., O. Suovaniemi, and M. Härkönen. "The role of pepsinogen assays as surrogate markers of gastritis dynamics in population studies." In Helicobactor pylori. Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-1763-2_12.

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Denimal, E., L. Nechak, J. J. Sinou, and S. Nacivet. "A New Surrogate Modeling Method Associating Generalized Polynomial Chaos Expansion and Kriging for Mechanical Systems Subjected to Friction-Induced Vibration." In Special Topics in Structural Dynamics, Volume 6. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53841-9_2.

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Morales, Xabier, Jordi Mill, Kristine A. Juhl, et al. "Deep Learning Surrogate of Computational Fluid Dynamics for Thrombus Formation Risk in the Left Atrial Appendage." In Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39074-7_17.

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Aumann, Quirin, Peter Benner, Jens Saak, and Julia Vettermann. "Model Order Reduction Strategies for the Computation of Compact Machine Tool Models." In Lecture Notes in Production Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-34486-2_10.

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AbstractThe deviation of the tool center point (TCP) of a machine tool from its desired location needs to be assessed correctly to ensure an accurate and safe operation of the machine. A major source of TCP deviation are thermal loads, which are constantly changing during operation. Numerical simulation models help predicting these loads, but are typically large and expensive to solve. Especially in (real-time feedback) control settings, but also to ensure an efficient design phase of machine tools, it is inevitable to use compact reduced-order surrogate models which approximate the behavior o
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Conference papers on the topic "Surrogate dynamics"

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Alfaham, Abdallah, and Siegfried Mercelis. "Artificial Surrogate Model for Computational Fluid Dynamics." In ESANN 2025. Ciaco - i6doc.com, 2025. https://doi.org/10.14428/esann/2025.es2025-70.

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LENGEL, RUSSELL, and JEFFREY LINDER. "The use of rubidium as a surrogate for potassium in combustion system imaging." In 21st Fluid Dynamics, Plasma Dynamics and Lasers Conference. American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-1547.

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Boopathy, Komahan, and Markus P. Rumpfkeil. "A Multivariate Interpolation and Regression Enhanced Kriging Surrogate Model." In 21st AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2964.

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Choze, Sergio, and Felipe A. Viana. "Simple and inexpensive algorithm for surrogate filtering." In 56th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2015. http://dx.doi.org/10.2514/6.2015-0139.

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Beyhaghi, Pooriya, Daniele Cavaglieri, and Thomas Bewley. "Delaunay-based Derivative-free Optimization via Global Surrogate, Part 1: Theory." In 21st AIAA Computational Fluid Dynamics Conference. American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-2707.

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Leifsson, Leifur, and Slawomir Koziel. "Surrogate-Based Shape Optimization of Low-Speed Wind Tunnel Contractions." In 42nd AIAA Fluid Dynamics Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2012. http://dx.doi.org/10.2514/6.2012-3344.

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Trizila, Patrick, Chang-Kwon Kang, Miguel Visbal, and Wei Shyy. "Unsteady Fluid Physics and Surrogate Modeling of Low Reynolds Number, Flapping Airfoils." In 38th Fluid Dynamics Conference and Exhibit. American Institute of Aeronautics and Astronautics, 2008. http://dx.doi.org/10.2514/6.2008-3821.

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Park, Chanyoung, Raphael T. Haftka, and Nam Ho Kim. "Simple Alternative to Bayesian Multi-Fidelity Surrogate Framework." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-0135.

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Chowdhury, Souma, Ali Mehmani, Weiyang Tong, and Achille Messac. "Adaptive Model Refinement in Surrogate-based Multiobjective Optimization." In 57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. American Institute of Aeronautics and Astronautics, 2016. http://dx.doi.org/10.2514/6.2016-0417.

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Pyle, James, Mozhgan Kabiri Chimeh, and Paul Richmond. "Surrogate Modelling for Efficient Discovery of Emergent Population Dynamics." In 2019 International Conference on High Performance Computing & Simulation (HPCS). IEEE, 2019. http://dx.doi.org/10.1109/hpcs48598.2019.9188208.

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Reports on the topic "Surrogate dynamics"

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Meidani, Hadi, and Amir Kazemi. Data-Driven Computational Fluid Dynamics Model for Predicting Drag Forces on Truck Platoons. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-036.

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Fuel-consumption reduction in the truck industry is significantly beneficial to both energy economy and the environment. Although estimation of drag forces is required to quantify fuel consumption of trucks, computational fluid dynamics (CFD) to meet this need is expensive. Data-driven surrogate models are developed to mitigate this concern and are promising for capturing the dynamics of large systems such as truck platoons. In this work, we aim to develop a surrogate-based fluid dynamics model that can be used to optimize the configuration of trucks in a robust way, considering various uncert
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Elliott, J. Hydra modeling of experiments to study ICF capsule fill hole dynamics using surrogate targets. Office of Scientific and Technical Information (OSTI), 2007. http://dx.doi.org/10.2172/925990.

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Torres, Marissa, Michael-Angelo Lam, and Matt Malej. Practical guidance for numerical modeling in FUNWAVE-TVD. Engineer Research and Development Center (U.S.), 2022. http://dx.doi.org/10.21079/11681/45641.

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This technical note describes the physical and numerical considerations for developing an idealized numerical wave-structure interaction modeling study using the fully nonlinear, phase-resolving Boussinesq-type wave model, FUNWAVE-TVD (Shi et al. 2012). The focus of the study is on the range of validity of input wave characteristics and the appropriate numerical domain properties when inserting partially submerged, impermeable (i.e., fully reflective) coastal structures in the domain. These structures include typical designs for breakwaters, groins, jetties, dikes, and levees. In addition to p
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Harris and Edlund. L51766 Instantaneous Rotational Velocity Development. Pipeline Research Council International, Inc. (PRCI), 1997. http://dx.doi.org/10.55274/r0010119.

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Considerable effort has been put forth to develop an automated method for balancing the power cylinders of reciprocating integral engines used in the natural gas industry. The benefits to power cylinder balance include reducted emissions and improved cylinder component mechanical integrity (which should lead to reductions in repair costs). The current approach to automate engine balancing uses pressure transducers to measure cylinder pressure, then integrate the signals into the engine fuel /timing management controller to achieve engine balance. Each power cylinder must be instrumented, which
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Flanagan Pritz, Colleen, Colleen Emery, Branden Johnson, et al. Sampling dragonflies for mercury analysis in Grand Canyon National Park, 2018–2024: A contribution of the Dragonfly Mercury Project. National Park Service, 2025. https://doi.org/10.36967/2310449.

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The Dragonfly Mercury Project is a collaborative initiative that utilizes dragonfly larvae as biosentinels to monitor mercury concentrations across 180 national parks and other protected lands, including Grand Canyon National Park (GRCA). These indicators serve as surrogates for environmental risk and can indicate where fish consumption could pose health risks through exposure to mercury. From 2018 to 2024, citizen scientists and staff from the National Park Service and U.S. Geological Survey helped collect close to 400 larvae from 25 GRCA sites across nearly 300 river miles, revealing mercury
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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maint
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