PDE-READ: human-readable partial differential equation discovery using deep learning
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Publication:6488684
DOI10.1016/J.NEUNET.2022.07.008WikidataQ113868209 ScholiaQ113868209MaRDI QIDQ6488684
Robert Stephany, Christopher J. Earls
Publication date: 17 October 2023
Published in: Neural Networks (Search for Journal in Brave)
sparse regressiondeep learningphysics-informed machine learningpartial differential equation discovery
Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- On the limited memory BFGS method for large scale optimization
- Spectral and finite difference solutions of the Burgers equations
- DeepMoD: deep learning for model discovery in noisy data
- Weak SINDy for partial differential equations
- Data-driven discovery of PDEs in complex datasets
- Nonlinear System Identification
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations
- Learning partial differential equations via data discovery and sparse optimization
- Robust and optimal sparse regression for nonlinear PDE models
- DL-PDE: Deep-Learning Based Data-Driven Discovery of Partial Differential Equations from Discrete and Noisy Data
- Gene selection for cancer classification using support vector machines
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