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Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)
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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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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
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
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
Ratcheting fluid pumps: Using generalized polynomial chaos expansions to assess pumping performance and sensitivity
Space‐time stochastic Galerkin boundary elements for acoustic scattering problems
Heiko Gimperlein, Fabian Meyer and Ceyhun Özdemir International Journal for Numerical Methods in Engineering 125(15) (2024) https://doi.org/10.1002/nme.7497
Generalized polynomial chaos expansions for the random fractional Bateman 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
Probabilistic-learning-based stochastic surrogate model from small incomplete datasets for nonlinear dynamical systems
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 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
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
Polynomial chaos expansion surrogate modeling of passive cardiac mechanics using the Holzapfel–Ogden constitutive model
J.O. Campos, R.M. Guedes, Y.B. Werneck, L.P.S. Barra, R.W. dos Santos and B.M. Rocha Journal of Computational Science 71 102039 (2023) https://doi.org/10.1016/j.jocs.2023.102039
Surface damage evolution of artillery barrel under high-temperature erosion and high-speed impact
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
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
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
Data-Driven Method to Quantify Correlated Uncertainties
Structure damage identification in dams using sparse polynomial chaos expansion combined with hybrid K-means clustering optimizer and genetic algorithm
Emulator-based Bayesian inference on non-proportional scintillation models by compton-edge probing
David Breitenmoser, Francesco Cerutti, Gernot Butterweck, Malgorzata Magdalena Kasprzak and Sabine Mayer Nature Communications 14(1) (2023) https://doi.org/10.1038/s41467-023-42574-y
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
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
Multi-parameter identification of concrete dam using polynomial chaos expansion and slime mould algorithm
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
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
A Polynomial-Chaos-Based Multifidelity Approach to the Efficient Uncertainty Quantification of Online Simulations of Automotive Propulsion Systems
Hang Yang, Alex Gorodetsky, Yuji Fujii and K. W. Wang Journal of Computational and Nonlinear Dynamics 17(5) (2022) https://doi.org/10.1115/1.4053559
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)
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
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
A General Framework of Rotational Sparse Approximation in Uncertainty Quantification
Efficient uncertainty propagation for photonics: Combining Implicit Semi-analog Monte Carlo (ISMC) and Monte Carlo generalised Polynomial Chaos (MC-gPC)
Nonlinear Vibrations of Simply Supported Cylindrical Panels with Uncertain Parameters: An Intrusive Application of the Generalized Polynomial Chaos Expansion
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 local hybrid surrogate‐based finite element tearing interconnecting dual‐primal method for nonsmooth random partial differential equations
Martin Eigel and Robert Gruhlke International Journal for Numerical Methods in Engineering 122(4) 1001 (2021) https://doi.org/10.1002/nme.6571
Stability analysis of a hyperbolic stochastic Galerkin formulation for the Aw-Rascle-Zhang model with relaxation
Stephan Gerster, Michael Herty and Elisa Iacomini Mathematical Biosciences and Engineering 18(4) 4372 (2021) https://doi.org/10.3934/mbe.2021220
Data-driven polynomial chaos expansions for characterization of complex fluid rheology: Case study of phosphate slurry