Deep-learning based discovery of partial differential equations in integral form from sparse and noisy data

DOI10.1016/j.jcp.2021.110592OpenAlexW3109497592WikidataQ115350064 ScholiaQ115350064MaRDI QIDQ2133022

Hao Xu, Nanzhe Wang, Dongxiao Zhang

Publication date: 29 April 2022

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

Full work available at URL: https://arxiv.org/abs/2011.11981

zbMATH Keywords

noisy dataintegral formPDE discoverydeep-learning

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 (2)

Group projected subspace pursuit for identification of variable coefficient differential equations (GP-IDENT) ⋮ Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data

Uses Software

• PDE-Net
• NETT

Cites Work

• Unnamed Item
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• Robust and optimal sparse regression for nonlinear PDE models
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• DL-PDE: Deep-Learning Based Data-Driven Discovery of Partial Differential Equations from Discrete and Noisy Data
• Data-driven discovery of governing equations for fluid dynamics based on molecular simulation
• Data-Driven Identification of Parametric Partial Differential Equations

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