shaoanlu / COVID-19-SINDYLinks
Use SINDY algorithm to discover a dynamical system from coronavirus data
☆13Updated last year
Alternatives and similar repositories for COVID-19-SINDY
Users that are interested in COVID-19-SINDY are comparing it to the libraries listed below
Sorting:
- Machine learning algorithms for discovering dimensionless groups from simulation and experimental data☆13Updated 2 years ago
- ☆15Updated last year
- Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"☆28Updated 5 years ago
- ☆16Updated last year
- ☆21Updated 2 years ago
- ☆11Updated 4 years ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆16Updated 4 years ago
- Benchmark for learning stiff problems using physics-informed machine learning☆12Updated 3 years ago
- ☆73Updated 5 years ago
- ☆21Updated 4 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆30Updated 3 years ago
- Interpretable machine learning (symbolic regression) using Genetic programming/Gene expression programming and Sparse regression used …☆34Updated 4 years ago
- ☆29Updated 2 years ago
- Code for the paper "Rational neural networks", NeurIPS 2020☆27Updated 4 years ago
- Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems☆63Updated 5 years ago
- ☆10Updated 2 years ago
- ☆29Updated 7 years ago
- ☆33Updated last year
- a multiresolution convolutional autoencoder architecture☆22Updated 4 years ago
- A tool for generating PDEs ground truth datasets from ARCSim, FEniCS and SU2☆37Updated 4 years ago
- Code for the paper "Structure-preserving neural networks" published in Journal of Computational Physics (JCP).☆19Updated 2 years ago
- Solving High Dimensional Partial Differential Equations with Deep Neural Networks☆34Updated 3 years ago
- Deep learning framework for model reduction of dynamical systems☆21Updated 4 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆26Updated 3 years ago
- Reference implementation of Finite Element Networks as proposed in "Learning the Dynamics of Physical Systems from Sparse Observations wi…☆69Updated last year
- ☆41Updated 7 years ago
- ☆30Updated 3 years ago
- [ICLR 2024] Scaling physics-informed hard constraints with mixture-of-experts.☆34Updated last year
- Constructing linearizing transformations for reduced-order modeling of nonlinear dynamical systems☆10Updated last year