Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations-Reference-Cited by-同舟云学术

Physics-Guided Discovery of Highly Nonlinear Parametric Partial Differential Equations

Published:2023-08-04 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Luo Yingtao¹^ORCID,Liu Qiang²^ORCID,Chen Yuntian³^ORCID,Hu Wenbo⁴^ORCID,Tian Tian⁵^ORCID,Zhu Jun⁶^ORCID

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA, USA

2. CRIPAC, MAIS, Institute of Automation, Chinese Academy of Sciences, Beijing, China

3. Ningbo Institute of Digital Twin, Eastern Institute of Technology, Ningbo, China

4. Hefei University of Technology, Hefei, China

5. RealAI, Beijing, China

6. Tsinghua University, Beijing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3580305.3599466

Reference62 articles.

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2. Learning data-driven discretizations for partial differential equations

3. Filipe De Avila Belbute-Peres , Thomas Economon , and Zico Kolter . 2020 . Combining differentiable PDE solvers and graph neural networks for fluid flow prediction . In International Conference on Machine Learning. PMLR, 2402--2411 . Filipe De Avila Belbute-Peres, Thomas Economon, and Zico Kolter. 2020. Combining differentiable PDE solvers and graph neural networks for fluid flow prediction. In International Conference on Machine Learning. PMLR, 2402--2411.

4. Automated reverse engineering of nonlinear dynamical systems

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

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