Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
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Title
Uncertainty quantification for nonlinear solid mechanics using reduced order models with Gaussian process regression
Authors
Keywords
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Journal
COMPUTERS & MATHEMATICS WITH APPLICATIONS
Volume 149, Issue -, Pages 1-23
Publisher
Elsevier BV
Online
2023-09-06
DOI
10.1016/j.camwa.2023.08.016
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- (2020) Mariella Kast et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
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- (2020) Liu Yang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Non-intrusive reduced order modeling of unsteady flows using artificial neural networks with application to a combustion problem
- (2019) Qian Wang et al. JOURNAL OF COMPUTATIONAL PHYSICS
- Model order reduction for large-scale structures with local nonlinearities
- (2019) Zhenying Zhang et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- A nonintrusive reduced order modelling approach using Proper Orthogonal Decomposition and locally adaptive sparse grids
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- Projection-based model reduction: Formulations for physics-based machine learning
- (2018) Renee Swischuk et al. COMPUTERS & FLUIDS
- Greedy Nonintrusive Reduced Order Model for Fluid Dynamics
- (2018) Wang Chen et al. AIAA JOURNAL
- Data-driven reduced order modeling for time-dependent problems
- (2018) Mengwu Guo et al. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
- Data-driven operator inference for nonintrusive projection-based model reduction
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- A Survey of Projection-Based Model Reduction Methods for Parametric Dynamical Systems
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- Sensitivity analysis for multidimensional and functional outputs
- (2014) Fabrice Gamboa et al. Electronic Journal of Statistics
- Reduced order modelling for unsteady fluid flow using proper orthogonal decomposition and radial basis functions
- (2013) S. Walton et al. APPLIED MATHEMATICAL MODELLING
- Nonintrusive reduced-order modeling of parametrized time-dependent partial differential equations
- (2013) Christophe Audouze et al. NUMERICAL METHODS FOR PARTIAL DIFFERENTIAL EQUATIONS
- Nonlinear model order reduction based on local reduced-order bases
- (2012) David Amsallem et al. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
- Reduced Basis Approximation for Nonlinear Parametrized Evolution Equations based on Empirical Operator Interpolation
- (2012) Martin Drohmann et al. SIAM JOURNAL ON SCIENTIFIC COMPUTING
- Multivariate sensitivity analysis to measure global contribution of input factors in dynamic models
- (2010) Matieyendou Lamboni et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
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- (2010) Saifon Chaturantabut et al. SIAM JOURNAL ON SCIENTIFIC COMPUTING
- Global sensitivity analysis using polynomial chaos expansions
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