A Bayesian approach to multiscale inverse problems with on-the-fly scale determination

DOI10.1016/j.jcp.2016.08.031zbMath1380.62109OpenAlexW2509879919MaRDI QIDQ1674657

Louis Ellam, Nicholas Zabaras, Mark A. Girolami

Publication date: 26 October 2017

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2016.08.031

zbMATH Keywords

inverse problemsgroundwater flowwaveletmulti-resolution analysispermeabilitysequential Monte Carloelliptic PDEsBayesianmultiscale modelingadaptiveDarcy flowhierarchical

Mathematics Subject Classification ID

Bayesian inference (62F15) Inverse problems in geophysics (86A22) Inverse problems for PDEs (35R30) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21)

Related Items

Bayesian multiscale deep generative model for the solution of high-dimensional inverse problems, Observable dictionary learning for high-dimensional statistical inference, Special issue: Predictive multiscale materials modeling, Multi-agent reinforcement learning aided sampling algorithms for a class of multiscale inverse problems, Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversion, Demonstration of the relationship between sensitivity and identifiability for inverse uncertainty quantification, Bayesian inference of random fields represented with the Karhunen-Loève expansion

Uses Software

• SINTEF
• deal.ii
• PRMLT

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