Leveraging Bayesian analysis to improve accuracy of approximate models

DOI10.1016/j.jcp.2019.05.015zbMath1453.62417arXiv1905.08227OpenAlexW2991889589WikidataQ127806718 ScholiaQ127806718MaRDI QIDQ2222288

Chiyu Jiang, Daniel Livescu, Balasubramanya T. Nadiga

Publication date: 26 January 2021

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

Full work available at URL: https://arxiv.org/abs/1905.08227

zbMATH Keywords

Bayesian analysisneural networkturbulence modelingreduced order modelingsurrogate modelingReynolds-averaged Navier Stokes

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Bayesian inference (62F15) Statistical turbulence modeling (76F55) Statistical solutions of Navier-Stokes and related equations (76D06)

Uses Software

• Spalart-Allmaras

Cites Work

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• Turbulent Flows
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• Buoyancy-driven variable-density turbulence
• Orientation of eddy fluxes in geostrophic turbulence
• Approximation by superpositions of a sigmoidal function
• An adaptive Metropolis algorithm
• Spalart-Allmaras

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