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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
Subspace Reduction for Stochastic Planar Elasticity
A hyperbolicity-preserving discontinuous stochastic Galerkin scheme for uncertain hyperbolic systems of equations
Jakob Dürrwächter, Thomas Kuhn, Fabian Meyer, Louisa Schlachter and Florian Schneider Journal of Computational and Applied Mathematics 370 112602 (2020) https://doi.org/10.1016/j.cam.2019.112602
Optimal Bayesian experimental design for subsurface flow problems
Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability
Alexander Litvinenko, Dmitry Logashenko, Raul Tempone, Gabriel Wittum and David Keyes GEM - International Journal on Geomathematics 11(1) (2020) https://doi.org/10.1007/s13137-020-0147-1
Conformally mapped polynomial chaos expansions for Maxwell's source problem with random input data
Niklas Georg and Ulrich Römer International Journal of Numerical Modelling: Electronic Networks, Devices and Fields 33(6) (2020) https://doi.org/10.1002/jnm.2776
Numerical approximation of poroelasticity with random coefficients using Polynomial Chaos and Hybrid High-Order methods
Michele Botti, Daniele A. Di Pietro, Olivier Le Maître and Pierre Sochala Computer Methods in Applied Mechanics and Engineering 361 112736 (2020) https://doi.org/10.1016/j.cma.2019.112736
Uncertainty Propagation Using Polynomial Chaos Expansions for Extreme Sea Level Hazard Assessment: The Case of the Eastern Adriatic Meteotsunamis
A two-stage surrogate model for Neo-Hookean problems based on adaptive proper orthogonal decomposition and hierarchical tensor approximation
Steffen Kastian, Dieter Moser, Lars Grasedyck and Stefanie Reese Computer Methods in Applied Mechanics and Engineering 372 113368 (2020) https://doi.org/10.1016/j.cma.2020.113368
Mathematical modeling of adulthood obesity epidemic in Spain using deterministic, frequentist and Bayesian approaches
Current Trends in Dynamical Systems in Biology and Natural Sciences
Francesco Florian and Rossana Vermiglio SEMA SIMAI Springer Series, Current Trends in Dynamical Systems in Biology and Natural Sciences 21 205 (2020) https://doi.org/10.1007/978-3-030-41120-6_11
Uncertainty quantification in a hydrogen production system based on the solar hybrid sulfur process
Computing the density function of complex models with randomness by using polynomial expansions and the RVT technique. Application to the SIR epidemic model
Robust topology optimization for heat conduction with polynomial chaos expansion
André Jacomel Torii, Diogo Pereira da Silva Santos and Eduardo Morais de Medeiros Journal of the Brazilian Society of Mechanical Sciences and Engineering 42(6) (2020) https://doi.org/10.1007/s40430-020-02367-6
Uncertainty Management for Robust Industrial Design in Aeronautics
Chris Lacor and Éric Savin Notes on Numerical Fluid Mechanics and Multidisciplinary Design, Uncertainty Management for Robust Industrial Design in Aeronautics 140 687 (2019) https://doi.org/10.1007/978-3-319-77767-2_42
Some greedy algorithms for sparse polynomial chaos expansions
An efficient method for stochastic optimal control with joint chance constraints for nonlinear systems
Joel A. Paulson and Ali Mesbah International Journal of Robust and Nonlinear Control 29(15) 5017 (2019) https://doi.org/10.1002/rnc.3999
Optimization with constraints considering polymorphic uncertainties
Markus Mäck, Ismail Caylak, Philipp Edler, Steffen Freitag, Michael Hanss, Rolf Mahnken, Günther Meschke and Eduard Penner GAMM-Mitteilungen 42(1) (2019) https://doi.org/10.1002/gamm.201900005
Uncertainty quantification for nonlinear difference equations with dependent random inputs via a stochastic Galerkin projection technique
Asymptotic expansion for some local volatility models arising in finance
Sergio Albeverio, Francesco Cordoni, Luca Di Persio and Gregorio Pellegrini Decisions in Economics and Finance 42(2) 527 (2019) https://doi.org/10.1007/s10203-019-00247-w
Phase driven study for stochastic linear multi-dofs dynamic response
Uncertainty quantification of simulated biomechanical stimuli in coronary artery bypass grafts
Justin S. Tran, Daniele E. Schiavazzi, Andrew M. Kahn and Alison L. Marsden Computer Methods in Applied Mechanics and Engineering 345 402 (2019) https://doi.org/10.1016/j.cma.2018.10.024
Polynomial chaos expansions for dependent random variables
John D. Jakeman, Fabian Franzelin, Akil Narayan, Michael Eldred and Dirk Plfüger Computer Methods in Applied Mechanics and Engineering 351 643 (2019) https://doi.org/10.1016/j.cma.2019.03.049
Compressive Sensing Based Stochastic Economic Dispatch With High Penetration Renewables
Data-driven uncertainty quantification for Formula 1: Diffuser, wing tip and front wing variations
Richard Ahlfeld, Fabio Ciampoli, Marco Pietropaoli, Nick Pepper and Francesco Montomoli Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 233(6) 1495 (2019) https://doi.org/10.1177/0954407019835315
Combining Polynomial Chaos Expansions and the Random Variable Transformation Technique to Approximate the Density Function of Stochastic Problems, Including Some Epidemiological Models
Julia Calatayud Gregori, Benito M. Chen-Charpentier, Juan Carlos Cortés López and Marc Jornet Sanz Symmetry 11(1) 43 (2019) https://doi.org/10.3390/sym11010043
A polynomial chaos expansion in dependent random variables
Emerging Applications of Control and Systems Theory
Joel A. Paulson, Eranda Harinath, Lucas C. Foguth and Richard D. Braatz Lecture Notes in Control and Information Sciences - Proceedings, Emerging Applications of Control and Systems Theory 63 (2018) https://doi.org/10.1007/978-3-319-67068-3_5
Dimension adaptive finite difference decomposition using multiple sparse grids for stochastic computation
Computational uncertainty quantification for random non-autonomous second order linear differential equations via adapted gPC: a comparative case study with random Fröbenius method and Monte Carlo simulation
The polynomial chaos approach for reachable set propagation with application to chance‐constrained nonlinear optimal control under parametric uncertainties