A robust framework for identification of PDEs from noisy data

DOI10.1016/j.jcp.2021.110657OpenAlexW3198056353WikidataQ114163421 ScholiaQ114163421MaRDI QIDQ2133542

Yong-Ming Liu, Zhi-Ming Zhang

Publication date: 29 April 2022

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

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110657

zbMATH Keywords

uncertaintydynamical systempartial differential equationsparse regressionphysics discovery

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx) Partial differential equations of mathematical physics and other areas of application (35Qxx)

Related Items

Robust physics discovery via supervised and unsupervised pattern recognition using the Euler characteristic

Uses Software

• DiffSharp
• GitHub
• PDE-Net
• SciANN

Cites Work

• Unnamed Item
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• PDE-Net 2.0: learning PDEs from data with a numeric-symbolic hybrid deep network
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• On early stopping in gradient descent learning
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• Data-Driven Identification of Parametric Partial Differential Equations