PredictiveScienceLab / pift-paper-2023
Physics-informed information field theory - Solve inverse problems with built-in model form uncertainty estimation
☆12Updated last year
Alternatives and similar repositories for pift-paper-2023:
Users that are interested in pift-paper-2023 are comparing it to the libraries listed below
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆25Updated 3 years ago
- Gradient-based adaptive sampling algorithms for self-supervising PINNs☆24Updated last year
- Accompanyig code for "Training Physics-Informed Neural Networks: one learning to rule them all?"☆11Updated 2 years ago
- Sparsity-promoting Kernel Dynamic Mode Decomposition for Nonlinear Dynamical Systems☆28Updated 2 years ago
- PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning☆35Updated 3 years ago
- Boosting the training of physics informed neural networks with transfer learning☆26Updated 3 years ago
- Semi-supervised Invertible Neural Operators for Bayesian Inverse Problems☆13Updated 10 months ago
- ☆18Updated 4 years ago
- Codes associated with the manuscript titled "Multi-stage neural networks: Function approximator of machine precision"☆38Updated 11 months ago
- code☆13Updated last year
- Pytorch implementation of the DeepMoD algorithm: [arXiv:1904.09406]☆32Updated last year
- Physics-Constrained Bayesian Neural Network for Fluid Flow Reconstruction with Sparse and Noisy Data☆48Updated 4 years ago
- ☆22Updated 2 years ago
- Learning with Higher Expressive Power than Neural Networks (On Learning PDEs)☆15Updated 4 years ago
- Bayesian optimized physics-informed neural network for parameter estimation☆25Updated 4 months ago
- ☆21Updated 3 years ago
- Official Github Repository for paper "Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (…☆10Updated last year
- ☆10Updated 3 years ago
- Tutorial on Gaussian Processes☆62Updated 5 years ago
- SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations☆20Updated 2 years ago
- Deep learning assisted dynamic mode decomposition☆19Updated 3 years ago
- Machine learning of linear differential equations using Gaussian processes☆24Updated 6 years ago
- This repository contains the code and models for our paper "Investigating and Mitigating Failure Modes in Physics-informed Neural Network…☆18Updated last year
- ME 697 - Advanced Scientific Machine Learning☆21Updated last week
- ☆14Updated 3 years ago
- ☆13Updated 5 years ago
- The code enables to perform Bayesian inference in an efficient manner through the use of Hamiltonian Neural Networks (HNNs), Deep Neural …☆14Updated 2 years ago
- ☆27Updated 6 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆67Updated 2 years ago
- Dimension reduced surrogate construction for parametric PDE maps☆37Updated last week