Nonlinear sparse Bayesian learning for physics-based models

DOI10.1016/j.jcp.2020.109728OpenAlexW3045676472MaRDI QIDQ2126972

Rimple Sandhu, Chris Pettit, Dominique Poirel, Abhijit Sarkar, Mohammad Khalil

Publication date: 19 April 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2020.109728

zbMATH Keywords

inverse problemsBayesian inferenceBayesian model selectionsparse learningautomatic relevance determinationGaussian mixture-model

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Uses Software

• SciPy
• PMTK
• PRMLT

Cites Work

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• Data-driven model reduction for the Bayesian solution of inverse problems
• 10.1162/15324430152748236
• Trust Region Methods
• The Wiener--Askey Polynomial Chaos for Stochastic Differential Equations
• Non‐linear model reduction for uncertainty quantification in large‐scale inverse problems
• Bayesian Compressive Sensing Using Laplace Priors
• The Homogeneous Chaos

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