ebilionis / py-kle
Karhunen-Loeve Expansions for Gaussian Random Fields in Python
☆15Updated 10 years ago
Related projects: ⓘ
- Machine learning of linear differential equations using Gaussian processes☆22Updated 6 years ago
- Variational Neural Networks for the Solution of Partial Differential Equations☆8Updated 4 years ago
- A Python module that implements tools for the simulation and identification of random fields using the Karhunen-Loeve expansion represent…☆18Updated 8 years ago
- ☆13Updated 6 months ago
- Source code for deep learning-based reduced order models for nonlinear time-dependent parametrized PDEs. Available on doi.org/10.1007/s10…☆19Updated last year
- ☆24Updated 6 years ago
- Numerical Gaussian Processes for Time-dependent and Non-linear Partial Differential Equations☆66Updated 4 years ago
- Deep Learning application to the partial differential equations☆29Updated 6 years ago
- ☆60Updated 5 years ago
- Deep learning framework for model reduction of dynamical systems☆21Updated 3 years ago
- Adaptive multiphase flow through porous media☆27Updated 8 years ago
- Sparse Physics-based and Interpretable Neural Networks☆43Updated 2 years ago
- Pytorch implementation of "DeepFlow: History Matching in the Space of Deep Generative Models"☆32Updated 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…☆37Updated last year
- Coupled-Automatic-Numerical differentiation scheme for physics-informed neural network (can-PINN)☆23Updated 9 months ago
- The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-…☆16Updated 2 years ago
- Repository for sharing code and data assocaited with En-DeepONet architecture☆23Updated 8 months ago
- Tutorial on a number of topics in Deep Learning☆32Updated 4 years ago
- Bayesian deep convolutional encoder-decoder networks for surrogate modeling and uncertainty quantification☆100Updated 4 years ago
- ☆35Updated last year
- Accelerating Physics Informed Neural Networks (PINNs) using Meshless Discretizations☆20Updated last year
- Pytorch implementation of Bayesian physics-informed neural networks☆33Updated 3 years ago
- TensorFlow 2.0 implementation of Yibo Yang, Paris Perdikaris’s adversarial Uncertainty Quantification in Physics Informed Neural Networks…☆17Updated last year
- ☆50Updated 6 months ago
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆42Updated 4 years ago
- Solving PDEs with NNs☆43Updated last year
- Multifidelity DeepONet☆25Updated last year
- Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems☆12Updated 3 months ago
- Practicum on Supervised Learning in Function Spaces☆31Updated 2 years ago
- DeepGreen network written in Tensorflow 2☆27Updated 3 years ago