PhIMaL / DeePyMoDLinks
☆50Updated 2 years ago
Alternatives and similar repositories for DeePyMoD
Users that are interested in DeePyMoD are comparing it to the libraries listed below
Sorting:
- A library for dimensionality reduction on spatial-temporal PDE☆71Updated last month
- ☆118Updated 6 years ago
- ☆41Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- ☆110Updated 4 years ago
- Source code of "Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems."☆76Updated 9 months ago
- ☆54Updated 3 years ago
- ☆63Updated 6 years ago
- Solving PDEs with NNs☆55Updated 3 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- ☆200Updated 10 months ago
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆33Updated 2 years ago
- Applications of PINOs☆146Updated 3 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆149Updated 5 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆36Updated 4 years ago
- Reliable extrapolation of deep neural operators informed by physics or sparse observations☆28Updated 2 years ago
- Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems☆15Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Updated 5 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Source code of 'Deep transfer operator learning for partial differential equations under conditional shift'.☆77Updated 2 years ago
- Competitive Physics Informed Networks☆32Updated last year
- Python codes for Locally Adaptive Activation Function (LAAF) used in deep neural networks. Please cite this work as "A D Jagtap, K Kawa…☆43Updated 3 years ago
- ☆63Updated last year
- ☆44Updated 3 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆84Updated 3 years ago
- ☆40Updated 2 years ago
- ☆40Updated last year
- Turbulent flow network source code☆71Updated 10 months ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆62Updated 5 years ago