What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data

DOI10.1016/j.cma.2020.113228zbMath1506.65196OpenAlexW3045258037WikidataQ114015821 ScholiaQ114015821MaRDI QIDQ2020254

Troy Butler, Harri Hakula

Publication date: 23 April 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.2020.113228

zbMATH Keywords

inverse problemdata-consistentpullback measurepush-forward measure

Mathematics Subject Classification ID

Estimates of eigenvalues in context of PDEs (35P15) Convergence of probability measures (60B10) Numerical methods for eigenvalue problems for boundary value problems involving PDEs (65N25)

Related Items (3)

Parameter estimation with maximal updated densities ⋮ Learning quantities of interest from dynamical systems for observation-consistent inversion ⋮ drums

Uses Software

• Mathematica
• BayesDA

Cites Work

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• Finite element approximation of eigenvalue problems
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• Maximuma posterioriestimates in linear inverse problems with log-concave priors are proper Bayes estimators
• Asymptotic Statistics
• Eigenmodes of Isospectral Drums
• Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems
• On Choosing and Bounding Probability Metrics
• Can One Hear the Shape of a Drum?

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