XDFLYQ's repositories
Examples
All benchmarks, examples and applications cases to be run by Kratos. Note that unit tests are in Kratos repository and NOT here
CODES
Codes for some of my co-authored journal/conference papers
Matlab-Machine
哔哩哔哩视频代码
DRL
Deep Reinforcement Learning
pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
LSWR_loss_function_PINN
A kind of loss function based on Least Squares Weighted Residual method for computational solid mechanics
keras
Deep Learning for humans
transformers
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
annotated_deep_learning_paper_implementations
🧑🏫 59 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
minGPT
A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training
easyrobust
EasyRobust: an Easy-to-use library for state-of-the-art Robust Computer Vision Research with PyTorch.
CANN-1
when using, please cite "A new family of Constitutive Artificial Neural Networks towards automated model discovery", CMAME, https://arxiv.org/abs/2210.02202
PINN_Comp_Mech
PINN program for computational mechanics
PSO-PINN
Physics-Informed Neural Networks Trained with Particle Swarm Optimization
PINN_TFI-HSS
The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks
Strong-yet-ductile-nanolamellar-high-entropy-alloys-by-additive-manufacturing
Crystal plasticity finite element code, VUMAT file for Abaqus
LabelFree-DNN-Surrogate
Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning
PiNN
A Python library for building atomic neural networks
Road2Coding
编程之路
Adaptive_Activation_Functions
We proposed the simple adaptive activation functions deep neural networks. The proposed method is simple and easy to implement in any neural networks architecture.
Predictions-of-thermal-fields-in-additive-manufacturing
Predicting Thermal Fields in AdditiveManufacturing by FEM simulations andMachine Learning
Machine-Learning-for-Beginner-by-Python3
为机器学习的入门者提供多种基于实例的sklearn、TensorFlow以及自编函数(AnFany)的ML算法程序。
PGNN
Physics-guided Neural Networks (PGNN) : An Application In Lake Temperature Modelling