ModelsFLOW / HODMDLinks
HODMD algorithm from Le Clainche & Vega, SIAM J. on Appl. Dyn. Sys. 16(2), 882-925, 2017
☆13Updated 8 years ago
Alternatives and similar repositories for HODMD
Users that are interested in HODMD are comparing it to the libraries listed below
Sorting:
- ☆63Updated 6 years ago
- Dynamic Mode Decomposition (DMD)☆33Updated 4 years ago
- Pytorch implementation of Bayesian physics-informed neural networks☆70Updated 4 years ago
- ☆40Updated 2 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆107Updated 5 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆149Updated 5 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- Codes related to our paper "Sparse Polynomial Chaos Expansions via D-optimal Designs and Compressed Sensing." https://www.sciencedirect.…☆20Updated 6 years ago
- Multi-fidelity reduced-order surrogate modeling☆29Updated 7 months ago
- Tutorials and examples of advanced sampling methods for solving Bayesian Model Updating Problems☆40Updated last year
- hPINN: Physics-informed neural networks with hard constraints☆153Updated 4 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 4 months ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆51Updated 4 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆83Updated 3 years ago
- ☆26Updated 7 years ago
- ☆115Updated last year
- codes for PINNs☆12Updated 4 years ago
- A MATLAB implementation of the co-kriging process using the DACE toolbox☆41Updated 8 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 4 years ago
- ☆130Updated 3 years ago
- Code for Rice et al. 2020 "Analyzing Koopman approaches to physics-informed machine learning for long-term sea-surface temperature forcea…☆36Updated 4 months ago
- Source code for POD-DL-ROM: enhancing deep learning-based reduced order models for nonlinear parametrized PDEs by proper orthogonal decom…☆37Updated 2 years ago
- POD-PINN code and manuscript☆57Updated 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 2 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆62Updated 5 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Updated 5 years ago
- ☆165Updated 3 years ago
- Characterizing possible failure modes in physics-informed neural networks.☆147Updated 4 years ago