Code and hyperparameters for the paper "Generative Adversarial Networks"

Code for reproducing experiments in "Improved Training of Wasserstein GANs"

Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.

Resources and Implementations of Generative Adversarial Nets: GAN, DCGAN, WGAN, CGAN, InfoGAN

[IEEE TIP] "EnlightenGAN: Deep Light Enhancement without Paired Supervision" by Yifan Jiang, Xinyu Gong, Ding Liu, Yu Cheng, Chen Fang, Xiaohui Shen, Jianchao Yang, Pan Zhou, Zhangyang Wang

This is the code of my master thesis.

TensorFlow examples

hPINN: Physics-informed neural networks with hard constraints

Transfer learning for improved generalizability in causal physics-informed neural networks for beam simulations

C++ and Cuda ops for fused FourierKAN

An efficient pure-PyTorch implementation of Kolmogorov-Arnold Network (KAN).

Kolmogorov Arnold Networks

Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations

A library for scientific machine learning and physics-informed learning

when using, please cite "Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems", CMAME, https://doi.org/10.1016/j.cma.2022.115346

gPINN: Gradient-enhanced physics-informed neural networks

Implementation of the paper "Self-Adaptive Physics-Informed Neural Networks using a Soft Attention Mechanism" [AAAI-MLPS 2021]

Complex beam PDEs are solved using physics informed neural networks