Nonlinear input feature reduction for data-based physical modeling

DOI10.1016/J.JCP.2022.111832OpenAlexW4313265402MaRDI QIDQ2112546

Samir Beneddine

Publication date: 11 January 2023

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

Full work available at URL: https://arxiv.org/abs/2206.07400

zbMATH Keywords

mutual informationfeature selectiondimensional analysisdeep learningphysical modeling

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Markov processes (60Jxx) Multivariate analysis (62Hxx)

Uses Software

• PyTorch
• Adam

Cites Work

• Selection of relevant features and examples in machine learning
• Multilayer feedforward networks are universal approximators
• A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics
• Physics-informed neural networks for high-speed flows
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• A paradigm for data-driven predictive modeling using field inversion and machine learning
• Equitability, mutual information, and the maximal information coefficient
• Asymptotic evaluation of certain markov process expectations for large time. IV
• Approximation by superpositions of a sigmoidal function

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