A Bayesian Approach to Estimating Background Flows from a Passive Scalar

DOI10.1137/19M1267544zbMath1455.65205arXiv1808.01084OpenAlexW3048575408MaRDI QIDQ5119638

Jeff Borggaard, Justin Krometis, Nathan E. Glatt-Holtz

Publication date: 31 August 2020

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.01084

zbMATH Keywords

fluid turbulenceMarkov chain Monte Carlo (MCMC)passive scalarsBayesian statistical inversion

Mathematics Subject Classification ID

Bayesian inference (62F15) Monte Carlo methods (65C05) Statistical turbulence modeling (76F55) Numerical solutions to stochastic differential and integral equations (65C30) Forced convection (76R05) Numerical methods for inverse problems for boundary value problems involving PDEs (65N21) Numerical methods for ill-posed problems for boundary value problems involving PDEs (65N20)

Related Items (8)

Concurrent MultiParameter Learning Demonstrated on the Kuramoto--Sivashinsky Equation ⋮ On Bayesian consistency for flows observed through a passive scalar ⋮ On the accept-reject mechanism for Metropolis-Hastings algorithms ⋮ A statistical framework for domain shape estimation in Stokes flows ⋮ Two-scale coupling for preconditioned Hamiltonian Monte Carlo in infinite dimensions ⋮ Regularized ensemble Kalman methods for inverse problems ⋮ Mixing rates for Hamiltonian Monte Carlo algorithms in finite and infinite dimensions ⋮ Laplacian Smoothing Stochastic Gradient Markov Chain Monte Carlo

Uses Software

• BayesDA
• Julia
• NUTS

Cites Work

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