Pinned Repositories
Book-Mathmatical-Foundation-of-Reinforcement-Learning
This is the homepage of a new book entitled "Mathmatical Foundations of Reinforcement Learning."
Conservative_PINNs
We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.
deeponet
Learning nonlinear operators via DeepONet
DeepRL-Tutorials
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
DRL_solver
FEMLecture
a very simple and basic finite element programming code based on python
Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
MPM3D-F90
material point method
Physics-Informed-Deep-Learning-Solid-and-Fluid-Mechanics
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
Physics-informed-DeepONets
sudi2006435's Repositories
sudi2006435/Book-Mathmatical-Foundation-of-Reinforcement-Learning
This is the homepage of a new book entitled "Mathmatical Foundations of Reinforcement Learning."
sudi2006435/Conservative_PINNs
We propose a conservative physics-informed neural network (cPINN) on decompose domains for nonlinear conservation laws. The conservation property of cPINN is obtained by enforcing the flux continuity in the strong form along the sub-domain interfaces.
sudi2006435/deeponet
Learning nonlinear operators via DeepONet
sudi2006435/DeepRL-Tutorials
Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch
sudi2006435/DRL_solver
sudi2006435/FEMLecture
a very simple and basic finite element programming code based on python
sudi2006435/Learning-Python-Physics-Informed-Machine-Learning-PINNs-DeepONets
Physics Informed Machine Learning Tutorials (Pytorch and Jax)
sudi2006435/MPM3D-F90
material point method
sudi2006435/Physics-Informed-Deep-Learning-Solid-and-Fluid-Mechanics
Using Physics-Informed Deep Learning (PIDL) techniques (W-PINNs-DE & W-PINNs) to solve forward and inverse hydrodynamic shock-tube problems and plane stress linear elasticity boundary value problems
sudi2006435/Physics-informed-DeepONets
sudi2006435/PINNs-TF2.0
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
sudi2006435/Pix2Text
Pix In, Latex & Text Out. Recognize Chinese, English Texts, and Math Formulas from Images.
sudi2006435/reinforcement-learning
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
sudi2006435/Reinforcement-Learning-Notes
Reinforcement-Learning-Notes, start with MDP.
sudi2006435/RL