PhIMaL / DeePyMoD_tensorflow
This implementation of DeePyMoD is no longer maintained! We switched to a PyTorch based implementation: https://github.com/PhIMaL/DeePyMoD_torch
☆24Updated 4 years ago
Related projects: ⓘ
- Parametric Gaussian Process Regression for Big Data☆44Updated 4 years ago
- Code accompanying the paper 'Manifold MCMC methods for Bayesian inference in a wide class of diffusion models'☆10Updated last year
- ☆30Updated 2 years ago
- Sample code for the NIPS paper "Scalable Variational Inference for Dynamical Systems"☆26Updated 5 years ago
- PyTorch implementation of GMLS-Nets. Machine learning methods for scattered unstructured data sets. Methods for learning differential op…☆22Updated 10 months ago
- A pyTorch Extension for Applied Mathematics☆38Updated 4 years ago
- A pytorch version of hamiltonian monte carlo☆12Updated 5 years ago
- Dynamic mode decomposition in Python☆13Updated 9 years ago
- Deep Learning application to the partial differential equations☆29Updated 6 years ago
- A Discussion on Solving Partial Differential Equations using Neural Networks☆64Updated 5 years ago
- Use SINDY algorithm to discover a dynamical system from coronavirus data☆12Updated 5 months ago
- Solving stochastic differential equations and Kolmogorov equations by means of deep learning and Multilevel Monte Carlo simulation☆10Updated 3 years ago
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆31Updated 10 months ago
- Quasi-Newton Algorithm for Stochastic Optimization☆10Updated 2 years ago
- Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features☆23Updated 5 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆43Updated 6 years ago
- ☆68Updated 4 years ago
- Bayesian calibration using Tensorflow Probability☆34Updated 5 years ago
- jupyter notebooks for the neural nets and differential equation paper☆27Updated 3 years ago
- Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning (NeurIPS 2020)☆21Updated 2 years ago
- Parametric Gaussian Process Regression for Big Data (Matlab Version)☆24Updated 6 years ago
- Companion package of the review paper entitled 'High-dimensional Gaussian sampling: A review and a unifying approach based on a stochasti…☆12Updated last year
- A simple MCMC framework for training Gaussian processes adding functionality to GPy.☆21Updated 9 years ago
- Code for "Nonlinear stochastic modeling with Langevin regression" J. L. Callaham, J.-C. Loiseau, G. Rigas, and S. L. Brunton☆24Updated 2 years ago
- Auxiliary variable Markov chain Monte Carlo methods☆10Updated 6 years ago
- Scalable Log Determinants for Gaussian Process Kernel Learning (https://arxiv.org/abs/1711.03481) (NIPS 2017)☆18Updated 6 years ago
- Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"☆23Updated 4 years ago
- Code for the paper "Auto differentiable Ensemble Kalman Filters" (https://arxiv.org/abs/2107.07687), accepted for publication in SIAM Jou…☆28Updated 2 years ago
- Long-term probabilistic forecasting of quasiperiodic phenomena using Koopman theory☆34Updated 2 years ago
- a multiresolution convolutional autoencoder architecture☆18Updated 3 years ago