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

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CODES

Codes for some of my co-authored journal/conference papers

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Matlab-Machine

哔哩哔哩视频代码

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DRL

Deep Reinforcement Learning

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pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

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LSWR_loss_function_PINN

A kind of loss function based on Least Squares Weighted Residual method for computational solid mechanics

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keras

Deep Learning for humans

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transformers

🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

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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, ... 🧠

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minGPT

A minimal PyTorch re-implementation of the OpenAI GPT (Generative Pretrained Transformer) training

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easyrobust

EasyRobust: an Easy-to-use library for state-of-the-art Robust Computer Vision Research with PyTorch.

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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

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PINN_Comp_Mech

PINN program for computational mechanics

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PSO-PINN

Physics-Informed Neural Networks Trained with Particle Swarm Optimization

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PINN_TFI-HSS

The code for the paper Temperature field inversion of heat-source systems via physics-informed neural networks

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Strong-yet-ductile-nanolamellar-high-entropy-alloys-by-additive-manufacturing

Crystal plasticity finite element code, VUMAT file for Abaqus

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LabelFree-DNN-Surrogate

Surrogate Modeling for Fluid Flows Based on Physics-Constrained Label-Free Deep Learning

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PiNN

A Python library for building atomic neural networks

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Road2Coding

编程之路

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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.

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Predictions-of-thermal-fields-in-additive-manufacturing

Predicting Thermal Fields in AdditiveManufacturing by FEM simulations andMachine Learning

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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。

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PGNN

Physics-guided Neural Networks (PGNN) : An Application In Lake Temperature Modelling

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