Goal-oriented adaptive surrogate construction for stochastic inversion

DOI10.1016/j.cma.2018.04.045zbMath1441.65006arXiv1802.10487OpenAlexW2788860117MaRDI QIDQ1986228

Steven Mattis, Barbara I. Wohlmuth

Publication date: 8 April 2020

Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1802.10487

zbMATH Keywords

error estimationadaptive methodsuncertainty quantificationBayesian inversioncomputational engineering

Mathematics Subject Classification ID

Bayesian inference (62F15) Probabilistic models, generic numerical methods in probability and statistics (65C20) Response surface designs (62K20)

Related Items (7)

Polynomial chaos expansions for dependent random variables ⋮ The surrogate matrix methodology: low-cost assembly for isogeometric analysis ⋮ Goal-oriented adaptive modeling of random heterogeneous media and model-based multilevel Monte Carlo methods ⋮ Fast sampling of parameterised Gaussian random fields ⋮ Cholesky-Based Experimental Design for Gaussian Process and Kernel-Based Emulation and Calibration ⋮ Multilevel Adaptive Sparse Leja Approximations for Bayesian Inverse Problems ⋮ Reliability and efficiency of DWR-type a posteriori error estimates with smart sensitivity weight recovering

Uses Software

• EnKF
• FEniCS

Cites Work

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