cics-nd / cnn-surrogateLinks
Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification
☆107Updated 5 years ago
Alternatives and similar repositories for cnn-surrogate
Users that are interested in cnn-surrogate are comparing it to the libraries listed below
Sorting:
- ☆63Updated 6 years ago
- Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data☆150Updated 6 years ago
- A pyTorch Extension for Applied Mathematics☆40Updated 5 years ago
- Hidden physics models: Machine learning of nonlinear partial differential equations☆149Updated 5 years ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆50Updated 5 years ago
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆94Updated 2 years ago
- Physics-constrained auto-regressive convolutional neural networks for dynamical PDEs☆93Updated 3 years ago
- Deep Hidden Physics Models: Deep Learning of Nonlinear Partial Differential Equations☆285Updated 3 years ago
- Sparse Physics-based and Interpretable Neural Networks☆52Updated 4 years ago
- ☆26Updated 7 years ago
- PhyGeoNet: Physics-Informed Geometry-Adaptive Convolutional Neural Networks for Solving Parametric PDEs on Irregular Domain☆92Updated 5 years ago
- Source code for "Deep Dynamical Modeling and Control of Unsteady Fluid Flows" from NeurIPS 2018☆49Updated 7 years ago
- ☆50Updated 2 years ago
- Solving PDEs with NNs☆55Updated 3 years ago
- ☆42Updated 5 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆69Updated 5 years ago
- ☆118Updated 6 years ago
- Machine learning of linear differential equations using Gaussian processes☆26Updated 7 years ago
- PDE-Net: Learning PDEs from Data☆330Updated 4 years ago
- Deep learning library for solving differential equations on top of PyTorch.☆62Updated 5 years ago
- 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
- Multi-fidelity modeling using Gaussian processes and nonlinear auto-regressive schemes.☆71Updated 9 years ago
- A hands-on tutorial on supervised learning with Gaussian processes☆37Updated 6 years ago
- ☆13Updated 6 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
- Pytorch implementation of Bayesian physics-informed neural networks☆71Updated 4 years ago
- ☆40Updated 2 years ago
- Multi-fidelity Generative Deep Learning Turbulent Flows☆37Updated 5 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆87Updated 5 months ago
- ☆200Updated 10 months ago