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Journal articles on the topic 'Bayesian Stochastic Optimization Model'

Author: Grafiati

Published: 4 June 2021

Last updated: 6 September 2023

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1

Gavrilov, Andrey, Evgeny Loskutov, and Dmitry Mukhin. "Bayesian optimization of empirical model with state-dependent stochastic forcing." Chaos, Solitons & Fractals 104 (November 2017): 327–37. http://dx.doi.org/10.1016/j.chaos.2017.08.032.

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Mujumdar, P. P., and B. Nirmala. "A Bayesian Stochastic Optimization Model for a Multi-Reservoir Hydropower System." Water Resources Management 21, no. 9 (2006): 1465–85. http://dx.doi.org/10.1007/s11269-006-9094-3.

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Sha, Di, Kaan Ozbay, and Yue Ding. "Applying Bayesian Optimization for Calibration of Transportation Simulation Models." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (2020): 215–28. http://dx.doi.org/10.1177/0361198120936252.

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The parameters of a transportation simulation model need to pass through a careful calibration process to ensure that the model’s output is as close as possible to the actual system. Owing to the computationally expensive and black-box nature of a simulation model, there is a need for robust and efficient calibration algorithms. This paper proposes a Bayesian optimization framework for the high-dimensional calibration problem of transportation simulation models. Bayesian optimization uses acquisition functions to determine more promising values for future evaluation, instead of relying on loca

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Im, Jongbin, and Jungsun Park. "Stochastic structural optimization using particle swarm optimization, surrogate models and Bayesian statistics." Chinese Journal of Aeronautics 26, no. 1 (2013): 112–21. http://dx.doi.org/10.1016/j.cja.2012.12.022.

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Yin, Long, Sheng Zhang, Kun Xiang, et al. "A New Stochastic Process of Prestack Inversion for Rock Property Estimation." Applied Sciences 12, no. 5 (2022): 2392. http://dx.doi.org/10.3390/app12052392.

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In order to enrich the current prestack stochastic inversion theory, we propose a prestack stochastic inversion method based on adaptive particle swarm optimization combined with Markov chain Monte Carlo (MCMC). The MCMC could provide a stochastic optimization approach, and, with the APSO, have a better performance in global optimization methods. This method uses logging data to define a preprocessed model space. It also uses Bayesian statistics and Markov chains with a state transition matrix to update and evolve each generation population in the data domain, then adaptive particle swarm opti

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Grana, Dario, Leandro de Figueiredo, and Klaus Mosegaard. "Markov chain Monte Carlo for petrophysical inversion." GEOPHYSICS 87, no. 1 (2021): M13—M24. http://dx.doi.org/10.1190/geo2021-0177.1.

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Stochastic petrophysical inversion is a method used to predict reservoir properties from seismic data. Recent advances in stochastic optimization allow generating multiple realizations of rock and fluid properties conditioned on seismic data. To match the measured data and represent the uncertainty of the model variables, many realizations are generally required. Stochastic sampling and optimization of spatially correlated models are computationally demanding. Monte Carlo methods allow quantifying the uncertainty of the model variables but are impractical for high-dimensional models with spati

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Wadoux, Alexandre M. J. C., Gerard B. M. Heuvelink, Remko Uijlenhoet, and Sytze de Bruin. "Optimization of rain gauge sampling density for river discharge prediction using Bayesian calibration." PeerJ 8 (July 30, 2020): e9558. http://dx.doi.org/10.7717/peerj.9558.

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River discharges are often predicted based on a calibrated rainfall-runoff model. The major sources of uncertainty, namely input, parameter and model structural uncertainty must all be taken into account to obtain realistic estimates of the accuracy of discharge predictions. Over the past years, Bayesian calibration has emerged as a suitable method for quantifying uncertainty in model parameters and model structure, where the latter is usually modelled by an additive or multiplicative stochastic term. Recently, much work has also been done to include input uncertainty in the Bayesian framework

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Wang, Han, Yunhe Liu, Changchun Yin, Jinfeng Li, Yang Su, and Bin Xiong. "Stochastic inversion of magnetotelluric data using deep reinforcement learning." GEOPHYSICS 87, no. 1 (2021): E49—E61. http://dx.doi.org/10.1190/geo2020-0425.1.

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We have adopted a new tool to invert magnetotelluric data for the 1D model based on deep Q-networks (DQN), which works as a stochastic optimization method. By transforming the inversion problem into a Markov decision process, the tool learns by trial and error to find the optimal path for updating the model to fit the observed data. The DQN method converges to the target through different paths (e.g., Bayesian or other stochastic methods) and can partially provide the probability distribution of the inversion results, which can be used for uncertainty estimation. The DQN search space gradually

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Shao, Wei, and Guangbao Guo. "Multiple-Try Simulated Annealing Algorithm for Global Optimization." Mathematical Problems in Engineering 2018 (July 17, 2018): 1–11. http://dx.doi.org/10.1155/2018/9248318.

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Simulated annealing is a widely used algorithm for the computation of global optimization problems in computational chemistry and industrial engineering. However, global optimum values cannot always be reached by simulated annealing without a logarithmic cooling schedule. In this study, we propose a new stochastic optimization algorithm, i.e., simulated annealing based on the multiple-try Metropolis method, which combines simulated annealing and the multiple-try Metropolis algorithm. The proposed algorithm functions with a rapidly decreasing schedule, while guaranteeing global optimum values.

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Chen, Xiqun (Michael), Xiang He, Chenfeng Xiong, Zheng Zhu, and Lei Zhang. "A Bayesian Stochastic Kriging Optimization Model Dealing with Heteroscedastic Simulation Noise for Freeway Traffic Management." Transportation Science 53, no. 2 (2019): 545–65. http://dx.doi.org/10.1287/trsc.2018.0819.

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Mahmood, Tariq, Nasir Ali, Naveed Ishtiaq Chaudhary, Khalid Mehmood Cheema, Ahmad H. Milyani, and Muhammad Asif Zahoor Raja. "Novel Adaptive Bayesian Regularization Networks for Peristaltic Motion of a Third-Grade Fluid in a Planar Channel." Mathematics 10, no. 3 (2022): 358. http://dx.doi.org/10.3390/math10030358.

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In this presented communication, a novel design of intelligent Bayesian regularization backpropagation networks (IBRBNs) based on stochastic numerical computing is presented. The dynamics of peristaltic motion of a third-grade fluid in a planar channel is examined by IBRBNs using multilayer structure modeling competency of neural networks trained with efficient optimization ability of Bayesian regularization method. The reference dataset used as inputs and targets parameters of IBRBN has been obtained via the state-of-the-art Adams numerical method. The data of solution dynamics is created for

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Wu, Mingqi, Yinsen Miao, Neilkunal Panchal, et al. "Stochastic clustering and pattern matching for real-time geosteering." GEOPHYSICS 84, no. 5 (2019): ID13—ID24. http://dx.doi.org/10.1190/geo2018-0781.1.

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We have developed a Bayesian statistical framework for quantitative geosteering in real time. Two types of contemporary geosteering approaches, model based and stratification based, are introduced. The latter is formulated as a Bayesian optimization procedure: The log from a pilot reference well is used as a stratigraphic signature of the geologic structure in a given region; the observed log sequence acquired along the wellbore is projected into the stratigraphic domain given a proposed earth model and directional survey; the pattern similarity between the converted log and the signature is m

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Perdikaris, Paris, and George Em Karniadakis. "Model inversion via multi-fidelity Bayesian optimization: a new paradigm for parameter estimation in haemodynamics, and beyond." Journal of The Royal Society Interface 13, no. 118 (2016): 20151107. http://dx.doi.org/10.1098/rsif.2015.1107.

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We present a computational framework for model inversion based on multi-fidelity information fusion and Bayesian optimization. The proposed methodology targets the accurate construction of response surfaces in parameter space, and the efficient pursuit to identify global optima while keeping the number of expensive function evaluations at a minimum. We train families of correlated surrogates on available data using Gaussian processes and auto-regressive stochastic schemes, and exploit the resulting predictive posterior distributions within a Bayesian optimization setting. This enables a smart

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Nishimura, Haruki, and Mac Schwager. "SACBP: Belief space planning for continuous-time dynamical systems via stochastic sequential action control." International Journal of Robotics Research 40, no. 10-11 (2021): 1167–95. http://dx.doi.org/10.1177/02783649211037697.

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We propose a novel belief space planning technique for continuous dynamics by viewing the belief system as a hybrid dynamical system with time-driven switching. Our approach is based on the perturbation theory of differential equations and extends sequential action control to stochastic dynamics. The resulting algorithm, which we name SACBP, does not require discretization of spaces or time and synthesizes control signals in near real-time. SACBP is an anytime algorithm that can handle general parametric Bayesian filters under certain assumptions. We demonstrate the effectiveness of our approa

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Schmidt, Dominik, Katrin Kahlen, Christopher Bahr, and Matthias Friedel. "Towards a Stochastic Model to Simulate Grapevine Architecture: A Case Study on Digitized Riesling Vines Considering Effects of Elevated CO2." Plants 11, no. 6 (2022): 801. http://dx.doi.org/10.3390/plants11060801.

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Modeling plant growth, in particular with functional-structural plant models, can provide tools to study impacts of changing environments in silico. Simulation studies can be used as pilot studies for reducing the on-field experimental effort when predictive capabilities are given. Robust model calibration leads to less fragile predictions, while introducing uncertainties in predictions allows accounting for natural variability, resulting in stochastic plant growth models. In this study, stochastic model components that can be implemented into the functional-structural plant model Virtual Ries

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Peng, Yong, Wei Xu, and Xiaoli Zhang. "An aggregation-decomposition bayesian stochastic optimization model for cascade hydropower reservoirs using medium-range precipitation forecasts." Journal of Physics: Conference Series 887 (August 2017): 012005. http://dx.doi.org/10.1088/1742-6596/887/1/012005.

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Enemark, Trine, Luk JM Peeters, Dirk Mallants, Okke Batelaan, Andrew P. Valentine, and Malcolm Sambridge. "Hydrogeological Bayesian Hypothesis Testing through Trans-Dimensional Sampling of a Stochastic Water Balance Model." Water 11, no. 7 (2019): 1463. http://dx.doi.org/10.3390/w11071463.

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Conceptual uncertainty is considered one of the major sources of uncertainty in groundwater flow modelling. In this regard, hypothesis testing is essential to increase system understanding by refuting alternative conceptual models. Often a stepwise approach, with respect to complexity, is promoted but hypothesis testing of simple groundwater models is rarely applied. We present an approach to model-based Bayesian hypothesis testing in a simple groundwater balance model, which involves optimization of a model in function of both parameter values and conceptual model through trans-dimensional sa

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Landi, Filippo, Francesca Marsili, Noemi Friedman, and Pietro Croce. "gPCE-Based Stochastic Inverse Methods: A Benchmark Study from a Civil Engineer’s Perspective." Infrastructures 6, no. 11 (2021): 158. http://dx.doi.org/10.3390/infrastructures6110158.

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In civil and mechanical engineering, Bayesian inverse methods may serve to calibrate the uncertain input parameters of a structural model given the measurements of the outputs. Through such a Bayesian framework, a probabilistic description of parameters to be calibrated can be obtained; this approach is more informative than a deterministic local minimum point derived from a classical optimization problem. In addition, building a response surface surrogate model could allow one to overcome computational difficulties. Here, the general polynomial chaos expansion (gPCE) theory is adopted with th

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Xu, Wei, Chi Zhang, Yong Peng, Guangtao Fu, and Huicheng Zhou. "A two stage Bayesian stochastic optimization model for cascaded hydropower systems considering varying uncertainty of flow forecasts." Water Resources Research 50, no. 12 (2014): 9267–86. http://dx.doi.org/10.1002/2013wr015181.

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Privas, Edwin, Cyrille De Saint Jean, and Gilles Noguere. "On the use of the BMC to resolve Bayesian inference with nuisance parameters." EPJ Nuclear Sciences & Technologies 4 (2018): 36. http://dx.doi.org/10.1051/epjn/2018042.

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Nuclear data are widely used in many research fields. In particular, neutron-induced reaction cross sections play a major role in safety and criticality assessment of nuclear technology for existing power reactors and future nuclear systems as in Generation IV. Because both stochastic and deterministic codes are becoming very efficient and accurate with limited bias, nuclear data remain the main uncertainty sources. A worldwide effort is done to make improvement on nuclear data knowledge thanks to new experiments and new adjustment methods in the evaluation processes. This paper gives an overv

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Fouskakis, D. "Bayesian variable selection in generalized linear models using a combination of stochastic optimization methods." European Journal of Operational Research 220, no. 2 (2012): 414–22. http://dx.doi.org/10.1016/j.ejor.2012.01.040.

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22

Merlé, Yann, and France Mentré. "Stochastic optimization algorithms of a Bayesian design criterion for Bayesian parameter estimation of nonlinear regression models: Application in pharmacokinetics." Mathematical Biosciences 144, no. 1 (1997): 45–70. http://dx.doi.org/10.1016/s0025-5564(97)00017-5.

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Martelli, Saulo, Daniela Calvetti, Erkki Somersalo, and Marco Viceconti. "Stochastic modelling of muscle recruitment during activity." Interface Focus 5, no. 2 (2015): 20140094. http://dx.doi.org/10.1098/rsfs.2014.0094.

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Muscle forces can be selected from a space of muscle recruitment strategies that produce stable motion and variable muscle and joint forces. However, current optimization methods provide only a single muscle recruitment strategy. We modelled the spectrum of muscle recruitment strategies while walking. The equilibrium equations at the joints, muscle constraints, static optimization solutions and 15-channel electromyography (EMG) recordings for seven walking cycles were taken from earlier studies. The spectrum of muscle forces was calculated using Bayesian statistics and Markov chain Monte Carlo

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Smith, Rory J. E., Gregory Ashton, Avi Vajpeyi, and Colm Talbot. "Massively parallel Bayesian inference for transient gravitational-wave astronomy." Monthly Notices of the Royal Astronomical Society 498, no. 3 (2020): 4492–502. http://dx.doi.org/10.1093/mnras/staa2483.

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ABSTRACT Understanding the properties of transient gravitational waves (GWs) and their sources is of broad interest in physics and astronomy. Bayesian inference is the standard framework for astrophysical measurement in transient GW astronomy. Usually, stochastic sampling algorithms are used to estimate posterior probability distributions over the parameter spaces of models describing experimental data. The most physically accurate models typically come with a large computational overhead which can render data analsis extremely time consuming, or possibly even prohibitive. In some cases highly

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Li, Chunyuan, Changyou Chen, Yunchen Pu, Ricardo Henao, and Lawrence Carin. "Communication-Efficient Stochastic Gradient MCMC for Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 4173–80. http://dx.doi.org/10.1609/aaai.v33i01.33014173.

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Learning probability distributions on the weights of neural networks has recently proven beneficial in many applications. Bayesian methods such as Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) offer an elegant framework to reason about model uncertainty in neural networks. However, these advantages usually come with a high computational cost. We propose accelerating SG-MCMC under the masterworker framework: workers asynchronously and in parallel share responsibility for gradient computations, while the master collects the final samples. To reduce communication overhead, two protocols

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ZHANG, Jian, Yanlong JIANG, Wei SUN, Hua LIU, Guodong LI, and Jiayong WANG. "Adaptive Powell’s Identification of Elastic Constants of Composite Glass Girder with Layered Shell Element Theory." Mechanics 26, no. 5 (2020): 390–97. http://dx.doi.org/10.5755/j01.mech.26.5.27873.

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For the composite glass box girder, the generalized Bayesian objective function of elastic constants of the structure was derived based on layered shell element theory. Mechanical performances of the composite glass box girder were solved by layered shell element method. Combined with quadratic parabolic interpolation search scheme of optimized step length, the adaptive Powell’s optimization theory was taken to complete the stochastic identification of elastic constants of composite glass box girder. Then the adaptive Powell’s identification steps of elastic constants of the structure were pre

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Wang, Huawei, Jun Gao, and Zhiyong Liu. "Maintenance Decision Based on Data Fusion of Aero Engines." Mathematical Problems in Engineering 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/628792.

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Maintenance has gained a great importance as a support function for ensuring aero engine reliability and availability. Cost-effectiveness and risk control are two basic criteria for accurate maintenance. Given that aero engines have much condition monitoring data, this paper presents a new condition-based maintenance decision system that employs data fusion for improving accuracy of reliability evaluation. Bayesian linear model has been applied, so that the performance degradation evaluation of aero engines could be realized. A reliability evaluation model has been presented based on gamma pro

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Ihou, Koffi Eddy, Manar Amayri, and Nizar Bouguila. "Stochastic Variational Optimization of a Hierarchical Dirichlet Process Latent Beta-Liouville Topic Model." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (2022): 1–48. http://dx.doi.org/10.1145/3502727.

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In topic models, collections are organized as documents where they arise as mixtures over latent clusters called topics. A topic is a distribution over the vocabulary. In large-scale applications, parametric or finite topic mixture models such as LDA (latent Dirichlet allocation) and its variants are very restrictive in performance due to their reduced hypothesis space. In this article, we address the problem related to model selection and sharing ability of topics across multiple documents in standard parametric topic models. We propose as an alternative a BNP (Bayesian nonparametric) topic m

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Liu, Baisen, Liangliang Wang, and Jiguo Cao. "Bayesian estimation of ordinary differential equation models when the likelihood has multiple local modes." Monte Carlo Methods and Applications 24, no. 2 (2018): 117–27. http://dx.doi.org/10.1515/mcma-2018-0010.

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Abstract Ordinary differential equations (ODEs) are popularly used to model complex dynamic systems by scientists; however, the parameters in ODE models are often unknown and have to be inferred from noisy measurements of the dynamic system. One conventional method is to maximize the likelihood function, but the likelihood function often has many local modes due to the complexity of ODEs, which makes the optimizing algorithm be vulnerable to trap in local modes. In this paper, we solve the global optimization issue of ODE parameters with the help of the Stochastic Approximation Monte Carlo (SA

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Watanabe, Sumio. "Information criteria and cross validation for Bayesian inference in regular and singular cases." Japanese Journal of Statistics and Data Science 4, no. 1 (2021): 1–19. http://dx.doi.org/10.1007/s42081-021-00121-3.

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AbstractIn data science, an unknown information source is estimated by a predictive distribution defined from a statistical model and a prior. In an older Bayesian framework, it was explained that the Bayesian predictive distribution should be the best on the assumption that a statistical model is convinced to be correct and a prior is given by a subjective belief in a small world. However, such a restricted treatment of Bayesian inference cannot be applied to highly complicated statistical models and learning machines in a large world. In 1980, a new scientific paradigm of Bayesian inference

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Teng, Tong, Jie Chen, Yehong Zhang, and Bryan Kian Hsiang Low. "Scalable Variational Bayesian Kernel Selection for Sparse Gaussian Process Regression." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5997–6004. http://dx.doi.org/10.1609/aaai.v34i04.6061.

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This paper presents a variational Bayesian kernel selection (VBKS) algorithm for sparse Gaussian process regression (SGPR) models. In contrast to existing GP kernel selection algorithms that aim to select only one kernel with the highest model evidence, our VBKS algorithm considers the kernel as a random variable and learns its belief from data such that the uncertainty of the kernel can be interpreted and exploited to avoid overconfident GP predictions. To achieve this, we represent the probabilistic kernel as an additional variational variable in a variational inference (VI) framework for SG

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Thompson, Bill, and Thomas L. Griffiths. "Human biases limit cumulative innovation." Proceedings of the Royal Society B: Biological Sciences 288, no. 1946 (2021): 20202752. http://dx.doi.org/10.1098/rspb.2020.2752.

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Is technological advancement constrained by biases in human cognition? People in all societies build on discoveries inherited from previous generations, leading to cumulative innovation. However, biases in human learning and memory may influence the process of knowledge transmission, potentially limiting this process. Here, we show that cumulative innovation in a continuous optimization problem is systematically constrained by human biases. In a large ( n = 1250) behavioural study using a transmission chain design, participants searched for virtual technologies in one of four environments afte

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Borisyak, Maxim, Tatiana Gaintseva, and Andrey Ustyuzhanin. "Adaptive divergence for rapid adversarial optimization." PeerJ Computer Science 6 (May 18, 2020): e274. http://dx.doi.org/10.7717/peerj-cs.274.

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Adversarial Optimization provides a reliable, practical way to match two implicitly defined distributions, one of which is typically represented by a sample of real data, and the other is represented by a parameterized generator. Matching of the distributions is achieved by minimizing a divergence between these distribution, and estimation of the divergence involves a secondary optimization task, which, typically, requires training a model to discriminate between these distributions. The choice of the model has its trade-off: high-capacity models provide good estimations of the divergence, but

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Mohamed, Linah, Mike Christie, and Vasily Demyanov. "Comparison of Stochastic Sampling Algorithms for Uncertainty Quantification." SPE Journal 15, no. 01 (2009): 31–38. http://dx.doi.org/10.2118/119139-pa.

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Summary History matching and uncertainty quantification are two important research topics in reservoir simulation currently. In the Bayesian approach, we start with prior information about a reservoir (e.g., from analog outcrop data) and update our reservoir models with observations (e.g., from production data or time-lapse seismic). The goal of this activity is often to generate multiple models that match the history and use the models to quantify uncertainties in predictions of reservoir performance. A critical aspect of generating multiple history-matched models is the sampling algorithm us

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Rozos, Evangelos. "Machine Learning, Urban Water Resources Management and Operating Policy." Resources 8, no. 4 (2019): 173. http://dx.doi.org/10.3390/resources8040173.

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Meticulously analyzing all contemporaneous conditions and available options before taking operations decisions regarding the management of the urban water resources is a necessary step owing to water scarcity. More often than not, this analysis is challenging because of the uncertainty regarding inflows to the system. The most common approach to account for this uncertainty is to combine the Bayesian decision theory with the dynamic programming optimization method. However, dynamic programming is plagued by the curse of dimensionality, that is, the complexity of the method is proportional to t

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Dash, Sujata, Ajith Abraham, Ashish Kr Luhach, Jolanta Mizera-Pietraszko, and Joel JPC Rodrigues. "Hybrid chaotic firefly decision making model for Parkinson’s disease diagnosis." International Journal of Distributed Sensor Networks 16, no. 1 (2020): 155014771989521. http://dx.doi.org/10.1177/1550147719895210.

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Parkinson’s disease is found as a progressive neurodegenerative condition which affects motor circuit by the loss of up to 70% of dopaminergic neurons. Thus, diagnosing the early stages of incidence is of great importance. In this article, a novel chaos-based stochastic model is proposed by combining the characteristics of chaotic firefly algorithm with Kernel-based Naïve Bayes (KNB) algorithm for diagnosis of Parkinson’s disease at an early stage. The efficiency of the model is tested on a voice measurement dataset that is collected from “UC Irvine Machine Learning Repository.” The dynamics o

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Hauschild, M. W., M. Pelikan, K. Sastry, and D. E. Goldberg. "Using Previous Models to Bias Structural Learning in the Hierarchical BOA." Evolutionary Computation 20, no. 1 (2012): 135–60. http://dx.doi.org/10.1162/evco_a_00056.

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Estimation of distribution algorithms (EDAs) are stochastic optimization techniques that explore the space of potential solutions by building and sampling explicit probabilistic models of promising candidate solutions. While the primary goal of applying EDAs is to discover the global optimum or at least its accurate approximation, besides this, any EDA provides us with a sequence of probabilistic models, which in most cases hold a great deal of information about the problem. Although using problem-specific knowledge has been shown to significantly improve performance of EDAs and other evolutio

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Liu, Mingliang, and Dario Grana. "Stochastic nonlinear inversion of seismic data for the estimation of petroelastic properties using the ensemble smoother and data reparameterization." GEOPHYSICS 83, no. 3 (2018): M25—M39. http://dx.doi.org/10.1190/geo2017-0713.1.

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We have developed a new stochastic nonlinear inversion method for seismic reservoir characterization studies to jointly estimate elastic and petrophysical properties and to quantify their uncertainty. Our method aims to estimate multiple reservoir realizations of the entire set of reservoir properties, including seismic velocities, density, porosity, mineralogy, and saturation, by iteratively updating the initial ensemble of models based on the mismatch between their seismic response and the measured seismic data. The initial models are generated using geostatistical methods and the geophysica

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Wang, Mingzhi, and Weidong Wang. "An Inverse Method for Measuring Elastoplastic Properties of Metallic Materials Using Bayesian Model and Residual Imprint from Spherical Indentation." Materials 14, no. 23 (2021): 7105. http://dx.doi.org/10.3390/ma14237105.

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In this paper, an inverse method is proposed for measuring the elastoplastic properties of metallic materials using a spherical indentation experiment. In the new method, the elastoplastic parameters are correlated with sub-space coordinates of indentation imprints using proper orthogonal decomposition (POD), and inverse identification of material properties is solved using a statistical Bayesian framework. The advantage of the method is that model parameters in the numerical optimization process are treated as the stochastic variables, and potential uncertainties can be considered. The poster

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Castelli, Simone, and Andrea Belleri. "Framework for Identification and Prediction of Corrosion Degradation in a Steel Column through Machine Learning and Bayesian Updating." Applied Sciences 13, no. 7 (2023): 4646. http://dx.doi.org/10.3390/app13074646.

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In recent years, structural health monitoring, starting from accelerometric data, is a method which has become widely adopted. Among the available techniques, machine learning is one of the most innovative and promising, supported by the continuously increasing computational capacity of current computers. The present work investigates the potential benefits of a framework based on supervised learning suitable for quantifying the corroded thickness of a structural system, herein uniformly applied to a reference steel column. The envisaged framework follows a hybrid approach where the training d

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Hernández, Felipe, and Xu Liang. "Hybridizing Bayesian and variational data assimilation for high-resolution hydrologic forecasting." Hydrology and Earth System Sciences 22, no. 11 (2018): 5759–79. http://dx.doi.org/10.5194/hess-22-5759-2018.

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Abstract. The success of real-time estimation and forecasting applications based on geophysical models has been possible thanks to the two main existing frameworks for the determination of the models' initial conditions: Bayesian data assimilation and variational data assimilation. However, while there have been efforts to unify these two paradigms, existing attempts struggle to fully leverage the advantages of both in order to face the challenges posed by modern high-resolution models – mainly related to model indeterminacy and steep computational requirements. In this article we introduce a

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Avadhanula, Vashist, Andrea Celli, Riccardo Colini-Baldeschi, Stefano Leonardi, and Matteo Russo. "Fully Dynamic Online Selection through Online Contention Resolution Schemes." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 6693–700. http://dx.doi.org/10.1609/aaai.v37i6.25821.

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We study fully dynamic online selection problems in an adversarial/stochastic setting that includes Bayesian online selection, prophet inequalities, posted price mechanisms, and stochastic probing problems subject to combinatorial constraints. In the classical ``incremental'' version of the problem, selected elements remain active until the end of the input sequence. On the other hand, in the fully dynamic version of the problem, elements stay active for a limited time interval, and then leave. This models, for example, the online matching of tasks to workers with task/worker-dependent working

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Park, Mijung, James Foulds, Kamalika Chaudhuri, and Max Welling. "Variational Bayes In Private Settings (VIPS)." Journal of Artificial Intelligence Research 68 (May 5, 2020): 109–57. http://dx.doi.org/10.1613/jair.1.11763.

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Many applications of Bayesian data analysis involve sensitive information such as personal documents or medical records, motivating methods which ensure that privacy is protected. We introduce a general privacy-preserving framework for Variational Bayes (VB), a widely used optimization-based Bayesian inference method. Our framework respects differential privacy, the gold-standard privacy criterion, and encompasses a large class of probabilistic models, called the Conjugate Exponential (CE) family. We observe that we can straightforwardly privatise VB’s approximate posterior distributions for m

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Guo, Yuxue, Xinting Yu, Yue-Ping Xu, Hao Chen, Haiting Gu, and Jingkai Xie. "AI-based techniques for multi-step streamflow forecasts: application for multi-objective reservoir operation optimization and performance assessment." Hydrology and Earth System Sciences 25, no. 11 (2021): 5951–79. http://dx.doi.org/10.5194/hess-25-5951-2021.

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Abstract. Streamflow forecasts are traditionally effective in mitigating water scarcity and flood defense. This study developed an artificial intelligence (AI)-based management methodology that integrated multi-step streamflow forecasts and multi-objective reservoir operation optimization for water resource allocation. Following the methodology, we aimed to assess forecast quality and forecast-informed reservoir operation performance together due to the influence of inflow forecast uncertainty. Varying combinations of climate and hydrological variables were input into three AI-based models, na

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Mitsuhashi, Yuta, Gaku Hashimoto, Hiroshi Okuda, and Fujio Uchiyama. "Stochastic Analysis of the Kamishiro Earthquake Considering a Dynamic Fault Rupture." Journal of Earthquake and Tsunami 12, no. 04 (2018): 1841009. http://dx.doi.org/10.1142/s1793431118410099.

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In recent years, a new demand has appeared for evaluations of earthquake fault displacements, to address the need to evaluate the soundness of underground structures. Fault displacements are caused by the rupturing of earthquake source faults, and are investigated through the use of methods such as the finite difference method and the finite element method (FEM). We conducted dynamic rupture simulations on the Kamishiro Fault Earthquake using a nonlinear FEM, focused on time history of fault displacement and response displacement, and demonstrated an ability to simulate observed values to a ce

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Adumene, Sidum, Rabiul Islam, Ibitoru Festus Dick, Esmaeil Zarei, Morrison Inegiyemiema, and Ming Yang. "Influence-Based Consequence Assessment of Subsea Pipeline Failure under Stochastic Degradation." Energies 15, no. 20 (2022): 7460. http://dx.doi.org/10.3390/en15207460.

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The complexity of corrosion mechanisms in harsh offshore environments poses safety and integrity challenges to oil and gas operations. Exploring the unstable interactions and complex mechanisms required an advanced probabilistic model. The current study presents the development of a probabilistic approach for a consequence-based assessment of subsea pipelines exposed to complex corrosion mechanisms. The Bayesian Probabilistic Network (BPN) is applied to structurally learn the propagation and interactions among under-deposit corrosion and microbial corrosion for the failure state prediction of

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Han, Qinghua, Minghai Pan, Weijun Long, Zhiheng Liang, and Chenggang Shan. "Joint Adaptive Sampling Interval and Power Allocation for Maneuvering Target Tracking in a Multiple Opportunistic Array Radar System." Sensors 20, no. 4 (2020): 981. http://dx.doi.org/10.3390/s20040981.

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In this paper, a joint adaptive sampling interval and power allocation (JASIPA) scheme based on chance-constraint programming (CCP) is proposed for maneuvering target tracking (MTT) in a multiple opportunistic array radar (OAR) system. In order to conveniently predict the maneuvering target state of the next sampling instant, the best-fitting Gaussian (BFG) approximation is introduced and used to replace the multimodal prior target probability density function (PDF) at each time step. Since the mean and covariance of the BFG approximation can be computed by a recursive formula, we can utilize

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T, Ermolieva, Ermoliev Y, Zagorodniy) A, et al. "Artificial Intelligence, Machine Learning, and Intelligent Decision Support Systems: Iterative “Learning” SQG-based procedures for Distributed Models’ Linkage." Artificial Intelligence 27, AI.2022.27(2) (2022): 92–97. http://dx.doi.org/10.15407/jai2022.02.092.

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In this paper we discuss the on-going joint work contributing to the IIASA (International Institute for Applied Systems Analysis, Laxenburg, Austria) and National Academy of Science of Ukraine projects on “Modeling and management of dynamic stochastic interdependent systems for food-water-energy-health security nexus” (see [1-2] and references therein). The project develops methodological and modeling tools aiming to create Intelligent multimodel Decision Support System (IDSS) and Platform (IDSP), which can integrate national Food, Water, Energy, Social models with the models operating at the

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Zhou, Sheng, Xin Wang, Jiajun Bu, et al. "DGE: Deep Generative Network Embedding Based on Commonality and Individuality." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 6949–56. http://dx.doi.org/10.1609/aaai.v34i04.6178.

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Network embedding plays a crucial role in network analysis to provide effective representations for a variety of learning tasks. Existing attributed network embedding methods mainly focus on preserving the observed node attributes and network topology in the latent embedding space, with the assumption that nodes connected through edges will share similar attributes. However, our empirical analysis of real-world datasets shows that there exist both commonality and individuality between node attributes and network topology. On the one hand, similar nodes are expected to share similar attributes

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Helander, Mary E., and Lawrence D. Stone. "Introduction: 2020 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research." INFORMS Journal on Applied Analytics 51, no. 5 (2021): 329–31. http://dx.doi.org/10.1287/inte.2021.1094.

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The judges for the 2020 Daniel H. Wagner Prize for Excellence in the Practice of Advanced Analytics and Operations Research selected the five finalist papers featured in this special issue of the INFORMS Journal on Applied Analytics (IJAA). The prestigious Wagner Prize—awarded for achievement in implemented operations research, management science, and advanced analytics—emphasizes the quality and originality of mathematical models along with clarity of written and oral exposition. This year’s winning application is a system for optimally managing Dow Agrosciences’ (now Corteva) seed corn portf

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