google / data-driven-discretization-1dLinks
Code for "Learning data-driven discretizations for partial differential equations"
☆167Updated 5 months ago
Alternatives and similar repositories for data-driven-discretization-1d
Users that are interested in data-driven-discretization-1d are comparing it to the libraries listed below
Sorting:
- ☆196Updated 4 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆149Updated 5 years ago
- ☆118Updated 6 years ago
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆285Updated 3 years ago
- ☆274Updated 3 years ago
- Efficient and Scalable Physics-Informed Deep Learning and Scientific Machine Learning on top of Tensorflow for multi-worker distributed c…☆117Updated 3 years ago
- ☆63Updated 6 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 years ago
- PyTorch-FEniCS interface☆106Updated 4 years ago
- Spectral Navier Stokes (and similar) solvers in Python☆343Updated 2 years ago
- A library for dimensionality reduction on spatial-temporal PDE☆71Updated last month
- Multi Fidelity Monte Carlo☆24Updated 6 years ago
- Methods and code for J. L. Callaham, J. N. Kutz, B. W. Brunton, and S. L. Brunton (2020)☆62Updated 5 years ago
- Neural network based solvers for partial differential equations and inverse problems . Implementation of physics-informed neural networks…☆159Updated last year
- Turbulent flow network source code☆71Updated 10 months ago
- Pseudospectral Kolmogorov Flow Solver☆42Updated 2 years ago
- ☆55Updated 2 years ago
- Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.☆79Updated 3 weeks ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- ☆50Updated 2 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆107Updated 5 years ago
- flowTorch - a Python library for analysis and reduced-order modeling of fluid flows☆166Updated this week
- Links to works on deep learning algorithms for physics problems, TUM-I15 and beyond☆60Updated 5 years ago
- Solving PDEs with NNs☆55Updated 3 years ago
- ☆42Updated 5 years ago
- High performance computational platform in Python for the spectral Galerkin method☆226Updated last month
- A Python package for spectral proper orthogonal decomposition (SPOD).☆116Updated 3 weeks ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- A Tensorflow re-implementation of the paper Convolutional Neural Networks for Steady Flow Approximation☆168Updated 7 years ago
- Python Active-subspaces Utility Library☆76Updated 5 years ago