DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning-Reference-Cited by-同舟云学术

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DISCOVER: Deep identification of symbolically concise open-form partial differential equations via enhanced reinforcement learning

Published:2024-02-20 Issue:1 Volume:6 Page:
ISSN:2643-1564
Container-title:Physical Review Research
language:en
Short-container-title:Phys. Rev. Research

Author:

Du Mengge^ORCID,Chen Yuntian^ORCID,Zhang Dongxiao^ORCID

Funder

National Center for Applied Mathematics Shenzhen

Shenzhen Key Laboratory of Natural Gas Hydrates

SUSTech – Qingdao New Energy Technology Research Institute

Publisher

American Physical Society (APS)

Link

http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevResearch.6.013182/fulltext

Reference63 articles.

1. Discovering governing equations from data by sparse identification of nonlinear dynamical systems

2. Advancing mathematics by guiding human intuition with AI

3. Learning emergent partial differential equations in a learned emergent space

4. Data-driven discovery of partial differential equations

5. SINDy-BVP: Sparse identification of nonlinear dynamics for boundary value problems

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1. Large language models for automatic equation discovery of nonlinear dynamics;Physics of Fluids;2024-09-01

2. Physics-constrained robust learning of open-form partial differential equations from limited and noisy data;Physics of Fluids;2024-05-01

3. Identification of partial differential equations from noisy data with integrated knowledge discovery and embedding using evolutionary neural networks;Theoretical and Applied Mechanics Letters;2024-03

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