sifanw094's Stars
google-research/google-research
Google Research
milesial/Pytorch-UNet
PyTorch implementation of the U-Net for image semantic segmentation with high quality images
yanx27/Pointnet_Pointnet2_pytorch
PointNet and PointNet++ implemented by pytorch (pure python) and on ModelNet, ShapeNet and S3DIS.
phlippe/uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
ashawkey/torch-ngp
A pytorch CUDA extension implementation of instant-ngp (sdf and nerf), with a GUI.
probml/pml2-book
Probabilistic Machine Learning: Advanced Topics
google/mipnerf
DedalusProject/dedalus
A flexible framework for solving PDEs with modern spectral methods.
tumaer/JAXFLUIDS
Differentiable Fluid Dynamics Package
spectralDNS/spectralDNS
Spectral Navier Stokes (and similar) solvers in Python
PredictiveIntelligenceLab/Physics-informed-DeepONets
clawpack/riemann_book
An interactive book about the Riemann problem for hyperbolic PDEs, using Jupyter notebooks.
matthias-wright/flaxmodels
Pretrained deep learning models for Jax/Flax: StyleGAN2, GPT2, VGG, ResNet, etc.
PredictiveIntelligenceLab/GradientPathologiesPINNs
PredictiveIntelligenceLab/CausalPINNs
PredictiveIntelligenceLab/MultiscalePINNs
pmocz/nbody-python
Vectorized N-body code (Python)
PredictiveIntelligenceLab/PINNsNTK
marinlauber/2D-Turbulence-Python
Simple OOP Python Code to run some Pseudo-Spectral 2D Simulations of Turbulence
fem-on-colab/fem-on-colab
PredictiveIntelligenceLab/Long-time-Integration-PI-DeepONets
PredictiveIntelligenceLab/TRIPODS_Winter_School_2022
Practicum on Supervised Learning in Function Spaces
PredictiveIntelligenceLab/ImprovedDeepONets
PredictiveIntelligenceLab/DeepStefan
PredictiveIntelligenceLab/PDE-constrained-optimization-PI-DeepONet
bhavnicksm/vanilla-transformer-jax
JAX/Flax implimentation of 'Attention Is All You Need' by Vaswani et al. (https://arxiv.org/abs/1706.03762)
sifanw094/vanilla-transformer-jax
JAX/Flax implimentation of 'Attention Is All You Need' by Vaswani et al. (https://arxiv.org/abs/1706.03762)