\texttt{Weak-PDE-LEARN}: a weak form based approach to discovering PDEs from noisy, limited data
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Publication:6498485
DOI10.1016/J.JCP.2024.112950MaRDI QIDQ6498485
Robert Stephany, Christopher J. Earls
Publication date: 7 May 2024
Published in: Journal of Computational Physics (Search for Journal in Brave)
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)
Cites Work
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- Discovering governing equations from data by sparse identification of nonlinear dynamical systems
- The route to chaos for the Kuramoto-Sivashinsky equation
- On the limited memory BFGS method for large scale optimization
- Spectral and finite difference solutions of the Burgers equations
- DLGA-PDE: discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm
- DeepMoD: deep learning for model discovery in noisy data
- Weak SINDy for partial differential equations
- Learning mean-field equations from particle data using WSINDy
- Data-driven discovery of PDEs in complex datasets
- Computational Inverse Techniques in Nondestructive Evaluation
- Automated reverse engineering of nonlinear dynamical systems
- Nonlinear analysis of hydrodynamic instability in laminar flames—I. Derivation of basic equations
- 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
- Bayesian deep learning for partial differential equation parameter discovery with sparse and noisy data
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