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Offset-free stochastic quadratic dynamic matrix control formulations using polynomial chaos expansions
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Spectral Properties of Elementwise-Transformed Spiked Matrices
Calibrating a finite-strain phase-field model of fracture for bonded granular materials with uncertainty quantification
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Explainable data-driven analysis of uncertainty propagation in 3D concrete printing via adaptive polynomial chaos expansion
Algorithm 1040: The Sparse Grids Matlab Kit - a Matlab implementation of sparse grids for high-dimensional function approximation and uncertainty quantification
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Polynomial-chaos-based conditional statistics for probabilistic learning with heterogeneous data applied to atomic collisions of Helium on graphite substrate
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
Low-rank solutions to the stochastic Helmholtz equation
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
Derivative-Enhanced Rational Polynomial Chaos for Uncertainty Quantification
Probabilistic Prequalification Scheme of a Distribution System Operator for Supporting Market Participation of Multiple Distributed Energy Resource Aggregators
An approximation theory framework for measure-transport sampling algorithms
Ricardo Baptista, Bamdad Hosseini, Nikola Kovachki, Youssef Marzouk and Amir Sagiv Mathematics of Computation 94(354) 1863 (2024) https://doi.org/10.1090/mcom/4013
On the convergence of the Galerkin method for random fractional differential equations
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
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
Finite-Horizon Robustness Analysis of an Automatic Landing System Under Probabilistic Uncertainty
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
Multigroup-like MC resolution of generalised Polynomial Chaos reduced models of the uncertain linear Boltzmann equation (+discussion on hybrid intrusive/non-intrusive uncertainty propagation)
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
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
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
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
The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory
Sergey Oladyshkin, Timothy Praditia, Ilja Kroeker, Farid Mohammadi, Wolfgang Nowak and Sebastian Otte Neural Networks 166 85 (2023) https://doi.org/10.1016/j.neunet.2023.06.036
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
Deriving task specific performance from the information processing capacity of a reservoir computer
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
Global sensitivity analysis using multi-resolution polynomial chaos expansion for coupled Stokes–Darcy flow problems
A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms
Paul-Christian Bürkner, Ilja Kröker, Sergey Oladyshkin and Wolfgang Nowak Journal of Computational Physics 488 112210 (2023) https://doi.org/10.1016/j.jcp.2023.112210
Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm
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
Efficient uncertainty propagation for photonics: Combining Implicit Semi-analog Monte Carlo (ISMC) and Monte Carlo generalised Polynomial Chaos (MC-gPC)
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
A non-Gaussian stochastic model from limited observations using polynomial chaos and fractional moments
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)
Nonlinear Vibrations of Simply Supported Cylindrical Panels with Uncertain Parameters: An Intrusive Application of the Generalized Polynomial Chaos Expansion