Physics Guided Machine Learning for Variational Multiscale Reduced Order Modeling

DOI10.1137/22m1496360arXiv2205.12419OpenAlexW4379513584MaRDI QIDQ6097872

Alessandro Veneziani, Omer San, Traian Iliescu, Adil Rasheed, Shady E. Ahmed

Publication date: 7 June 2023

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2205.12419

zbMATH Keywords

autoencodervariational multiscale methodreduced order modelingGalerkin projectionnonlinear proper orthogonal decompositionphysics guided machine learning

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05) Variational methods applied to problems in fluid mechanics (76M30) Finite difference methods for initial value and initial-boundary value problems involving PDEs (65M06) Dynamical systems approach to turbulence (76F20) Neural nets and related approaches to inference from stochastic processes (62M45)

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