Recovering missing CFD data for high-order discretizations using deep neural networks and dynamics learning

DOI10.1016/j.jcp.2019.05.041zbMath1452.68147arXiv1812.01177OpenAlexW2949650570WikidataQ127710547 ScholiaQ127710547MaRDI QIDQ2222332

Liqian Peng, Jeremy Morton, F. D. Witherden, Kevin T. Carlberg, Mykel J. Kochenderfer, Anthony Jameson

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/1812.01177

zbMATH Keywords

machine learninghigh-order schemesCFDdeep learningautoencodersdynamics learning

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Learning and adaptive systems in artificial intelligence (68T05) Finite element methods applied to problems in fluid mechanics (76M10)

Related Items (9)

Nonintrusive Reduced Order Modelling of Convective Boussinesq Flows ⋮ Non-intrusive reduced-order modeling using uncertainty-aware deep neural networks and proper orthogonal decomposition: application to flood modeling ⋮ Machine learning for fluid flow reconstruction from limited measurements ⋮ Scalable \textit{in situ} compression of transient simulation data using time-dependent bases ⋮ Sparse identification of nonlinear dynamics with low-dimensionalized flow representations ⋮ Shallow neural networks for fluid flow reconstruction with limited sensors ⋮ Multi-level convolutional autoencoder networks for parametric prediction of spatio-temporal dynamics ⋮ A Tailored Convolutional Neural Network for Nonlinear Manifold Learning of Computational Physics Data Using Unstructured Spatial Discretizations ⋮ Data-driven predictions of the Lorenz system

Uses Software

• LIBSVM
• darch
• PyFR
• Adam

Cites Work

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