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Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of Helium on graphite substrate
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Heiko Gimperlein, Fabian Meyer and Ceyhun Özdemir International Journal for Numerical Methods in Engineering 125(15) (2024) https://doi.org/10.1002/nme.7497
An approximation theory framework for measure-transport sampling algorithms
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Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)
Paul Novello, Gaël Poëtte, David Lugato, Simon Peluchon and Pietro Marco Congedo Journal of Computational Physics 498 112700 (2024) https://doi.org/10.1016/j.jcp.2023.112700
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
Algorithm 1040: The Sparse Grids Matlab Kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
Chiara Piazzola and Lorenzo Tamellini ACM Transactions on Mathematical Software 50(1) 1 (2024) https://doi.org/10.1145/3630023
Energy stable and structure-preserving schemes for the stochastic Galerkin shallow water equations
Dihan Dai, Yekaterina Epshteyn and Akil Narayan ESAIM: Mathematical Modelling and Numerical Analysis 58(2) 723 (2024) https://doi.org/10.1051/m2an/2024012
Surrogate recycling for structures with spatially uncertain stiffness
Karl-Alexander Hoppe, Kevin Josef Li, Bettina Chocholaty, Johannes D. Schmid, Simon Schmid, Kian Sepahvand and Steffen Marburg Journal of Sound and Vibration 570 117997 (2024) https://doi.org/10.1016/j.jsv.2023.117997
Exact and Approximate Moment Derivation for Probabilistic Loops With Non-Polynomial Assignments
Andrey Kofnov, Marcel Moosbrugger, Miroslav Stankovič, Ezio Bartocci and Efstathia Bura ACM Transactions on Modeling and Computer Simulation 34(3) 1 (2024) https://doi.org/10.1145/3641545
Probabilistic Prequalification Scheme of a Distribution System Operator for Supporting Market Participation of Multiple Distributed Energy Resource Aggregators
Bayesian calibration with summary statistics for the prediction of xenon diffusion in UO2 nuclear fuel
Pieterjan Robbe, David Andersson, Luc Bonnet, Tiernan A. Casey, Michael W.D. Cooper, Christopher Matthews, Khachik Sargsyan and Habib N. Najm Computational Materials Science 225 112184 (2023) https://doi.org/10.1016/j.commatsci.2023.112184
STOCHASTIC POLYNOMIAL CHAOS EXPANSIONS TO EMULATE STOCHASTIC SIMULATORS
Gaussian active learning on multi-resolution arbitrary polynomial chaos emulator: concept for bias correction, assessment of surrogate reliability and its application to the carbon dioxide benchmark
Multigroup-like MC resolution of generalised Polynomial Chaos reduced models of the uncertain linear Boltzmann equation (+discussion on hybrid intrusive/non-intrusive uncertainty propagation)
Lianghao Cao, Thomas O'Leary-Roseberry, Prashant K. Jha, J. Tinsley Oden and Omar Ghattas Journal of Computational Physics 486 112104 (2023) https://doi.org/10.1016/j.jcp.2023.112104
Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm
Bayesian updating for predictions of delayed strains of large concrete structures: influence of prior distribution
D. Rossat, J. Baroth, M. Briffaut, F. Dufour, A. Monteil, B. Masson and S. Michel-Ponnelle European Journal of Environmental and Civil Engineering 27(4) 1763 (2023) https://doi.org/10.1080/19648189.2022.2095441
Global sensitivity analysis of a coupled multiphysics model to predict surface evolution in fusion plasma–surface interactions
Pieterjan Robbe, Sophie Blondel, Tiernan A. Casey, Ane Lasa, Khachik Sargsyan, Brian D. Wirth and Habib N. Najm Computational Materials Science 226 112229 (2023) https://doi.org/10.1016/j.commatsci.2023.112229
An Adaptive Sampling and Domain Learning Strategy for Multivariate Function Approximation on Unknown Domains
Metamodel-assisted hybrid optimization strategy for model updating using vibration response data
Li YiFei, Cao MaoSen, Tran N. Hoa, S. Khatir, Hoang-Le Minh, Thanh SangTo, Thanh Cuong-Le and Magd Abdel Wahab Advances in Engineering Software 185 103515 (2023) https://doi.org/10.1016/j.advengsoft.2023.103515
Global Sensitivity Analysis and Uncertainty Quantification for Background Solar Wind Using the Alfvén Wave Solar Atmosphere Model
Aniket Jivani, Nishtha Sachdeva, Zhenguang Huang, Yang Chen, Bart van der Holst, Ward Manchester, Daniel Iong, Hongfan Chen, Shasha Zou, Xun Huan and Gabor Toth Space Weather 21(1) (2023) https://doi.org/10.1029/2022SW003262
A polynomial chaos efficient global optimization approach for Bayesian optimal experimental design
André Gustavo Carlon, Cibelle Dias de Carvalho Dantas Maia, Rafael Holdorf Lopez, André Jacomel Torii and Leandro Fleck Fadel Miguel Probabilistic Engineering Mechanics 72 103454 (2023) https://doi.org/10.1016/j.probengmech.2023.103454
Data-Driven Method to Quantify Correlated Uncertainties
Approximating the first passage time density from data using generalized Laguerre polynomials
Elvira Di Nardo, Giuseppe D’Onofrio and Tommaso Martini Communications in Nonlinear Science and Numerical Simulation 118 106991 (2023) https://doi.org/10.1016/j.cnsns.2022.106991
Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification
A Comparative Study of Polynomial-Type Chaos Expansions for Indicator Functions
Florian Bourgey, Emmanuel Gobet and Clément Rey SIAM/ASA Journal on Uncertainty Quantification 10(4) 1350 (2022) https://doi.org/10.1137/21M1413146
Uncertainty Quantification Framework for Predicting Material Response with Large Number of Parameters: Application to Creep Prediction in Ferritic-Martensitic Steels Using Combined Crystal Plasticity and Grain Boundary Models
Amirfarzad Behnam, Timothy J. Truster, Ramakrishna Tipireddy, Mark C. Messner and Varun Gupta Integrating Materials and Manufacturing Innovation 11(4) 516 (2022) https://doi.org/10.1007/s40192-022-00277-0
Stochastic Galerkin Methods for Linear Stability Analysis of Systems with Parametric Uncertainty
Uncertainty consideration in CFD-models via response surface modeling: Application on realistic dense and light gas dispersion simulations
Ronald Zinke, Kevin Wothe, Dmitry Dugarev, Oliver Götze, Florian Köhler, Sebastian Schalau and Ulrich Krause Journal of Loss Prevention in the Process Industries 75 104710 (2022) https://doi.org/10.1016/j.jlp.2021.104710
Variational inference with NoFAS: Normalizing flow with adaptive surrogate for computationally expensive models
Nonlinear Vibrations of Simply Supported Cylindrical Panels with Uncertain Parameters: An Intrusive Application of the Generalized Polynomial Chaos Expansion
Numerical Analysis of the Monte-Carlo Noise for the Resolution of the Deterministic and Uncertain Linear Boltzmann Equation (Comparison of Non-Intrusive gPC and MC-gPC)
Global Sensitivity Analysis and Uncertainty Quantification for Simulated Atrial Electrocardiograms
Benjamin Winkler, Claudia Nagel, Nando Farchmin, Sebastian Heidenreich, Axel Loewe, Olaf Dössel and Markus Bär Metrology 3(1) 1 (2022) https://doi.org/10.3390/metrology3010001
Efficient uncertain keff computations with the Monte Carlo resolution of generalised Polynomial Chaos based reduced models
Efficient uncertainty propagation for photonics: Combining Implicit Semi-analog Monte Carlo (ISMC) and Monte Carlo generalised Polynomial Chaos (MC-gPC)
A Global Sensitivity Analysis Framework for Hybrid Simulation with Stochastic Substructures
Nikolaos Tsokanas, Xujia Zhu, Giuseppe Abbiati, Stefano Marelli, Bruno Sudret and Božidar Stojadinović Frontiers in Built Environment 7 (2021) https://doi.org/10.3389/fbuil.2021.778716