Woody2357 / 2022-Summer-Course
This is a repository of the supplementary implementation for the 2022 summer course 'Mathematical Theory and Applications of Deep Learning', taught by Professor Haizhao Yang at Tianyuan Mathematical Center in Central China
☆44Updated 2 years ago
Alternatives and similar repositories for 2022-Summer-Course
Users that are interested in 2022-Summer-Course are comparing it to the libraries listed below
Sorting:
- Tutorials on deep learning, Python, and dissipative particle dynamics☆188Updated 2 years ago
- Implementation of the Deep Ritz method and the Deep Galerkin method☆55Updated 4 years ago
- Group project for Deep Learning: Algorithms and Applications in Peking University 2018 Spring. This is a brief survey, discussion and imp…☆44Updated 6 years ago
- Examplary code for NN, MFNN, DynNet, PINNs and CNN☆49Updated 3 years ago
- PINN, DGM and DRM☆20Updated last year
- ☆49Updated 5 months ago
- Reproduce the first two numerical experiments(Pytorch)☆26Updated 4 years ago
- ☆42Updated last month
- ☆53Updated 3 months ago
- ☆26Updated 11 months ago
- DeepXDE and PINN☆109Updated 2 years ago
- hp-VPINNs: variational physics-informed neural network with domain decomposition is a general framework to solve differential equations☆79Updated 2 years ago
- ☆41Updated 2 years ago
- Non-adaptive and residual-based adaptive sampling for PINNs☆75Updated 2 years ago
- Pytorch implementation of Bayesian physics-informed neural networks☆59Updated 3 years ago
- ☆61Updated 2 years ago
- physics-informed neural network for elastodynamics problem☆138Updated 3 years ago
- This repository contains a number of Jupyter Notebooks illustrating different approaches to solve partial differential equations by means…☆177Updated 4 years ago
- DeepONet & FNO (with practical extensions)☆292Updated last year
- Sparse Physics-based and Interpretable Neural Networks☆48Updated 3 years ago
- ☆17Updated last year
- Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning☆88Updated last year
- Code for "Deep Nitsche Method: Deep Ritz Method with Essential Boundary Conditions"☆16Updated 2 years ago
- We develop a custom ML routine XNODE-WAN to solve parabolic PDEs with high efficiency.☆11Updated 2 years ago
- when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/1…☆69Updated 2 years ago
- U-FNO - an enhanced Fourier neural operator-based deep-learning model for multiphase flow☆134Updated 8 months ago
- Modified Meshgraphnets with more features☆42Updated 3 months ago
- ☆47Updated 2 years ago
- A pytorch implementaion of physics informed neural networks for two dimensional NS equation☆141Updated last year
- ☆29Updated last year