Surrogate-based sequential Bayesian experimental design using non-stationary Gaussian processes

DOI10.1016/j.cma.2021.114007zbMath1502.62085OpenAlexW3179686269MaRDI QIDQ2237721

Ilias Bilionis, Nimish M. Awalgaonkar, Panagiotis Tsilifis, Jitesh Panchal, Piyush Pandita

Publication date: 28 October 2021

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

Full work available at URL: https://doi.org/10.1016/j.cma.2021.114007

zbMATH Keywords

design of experimentsBayesian inferencesequential designsexpected information gainnon-stationary Gaussian processes

Mathematics Subject Classification ID

Gaussian processes (60G15) Bayesian inference (62F15) Sequential statistical design (62L05) Probabilistic methods, stochastic differential equations (65C99)

Related Items (3)

Variational Bayesian approximation of inverse problems using sparse precision matrices ⋮ Deep capsule encoder–decoder network for surrogate modeling and uncertainty quantification ⋮ Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian processes

Uses Software

• EGO

Cites Work

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