bdshaffer31
Machine learning for fluid flows and controls at UPenn. Public repos are for fun.
University of PennsylvaniaPhiladelphia
bdshaffer31's Stars
PredictiveIntelligenceLab/micrometer
ml-jku/UPT
Code for the paper Universal Physics Transformers
tqdm/tqdm
:zap: A Fast, Extensible Progress Bar for Python and CLI
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
kinnala/scikit-fem
Simple finite element assemblers
PredictiveIntelligenceLab/ENM-5310
natrask/ENM5310
marinlauber/2D-Turbulence-Python
Simple OOP Python Code to run some Pseudo-Spectral 2D Simulations of Turbulence
HIPS/autograd
Efficiently computes derivatives of NumPy code.
google/jax-cfd
Computational Fluid Dynamics in JAX
tum-pbs/PhiFlow
A differentiable PDE solving framework for machine learning
whitneychiu/lipmlp_pytorch
natrask/BracketGraphs
Code accompanying "Reversible and irreversible bracket-based dynamics for deep graph neural networks" NeurIPS 2023 paper.
Harry24k/bayesian-neural-network-pytorch
PyTorch implementation of bayesian neural network [torchbnn]
trevorstephens/gplearn
Genetic Programming in Python, with a scikit-learn inspired API
KindXiaoming/pykan
Kolmogorov Arnold Networks
mit-mseas/neuralClosureModels
Code for the framework, neural closure models.
zabaras/transformer-physx
Transformers for modeling physical systems
leap-stc/ClimSim
An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators.
rtqichen/torchdiffeq
Differentiable ODE solvers with full GPU support and O(1)-memory backpropagation.
SpeedyWeather/SpeedyWeather.jl
Play atmospheric modelling like it's LEGO.
smowlavi/CoherentStructures
Algorithms for identifying coherent structures in sparse and noisy trajectory datasets
GregTJ/stable-fluids
A minimal Stable Fluids inspired fluid solver with Python and NumPy.
nasimrahaman/SpectralBias
Code for "On the Spectral Bias of Neural Networks", to appear in ICML 2019 (Long Beach, CA).
JRice15/physics-informed-autoencoders
Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid flow dynamics
PyDMD/PyDMD
Python Dynamic Mode Decomposition
ehsanhaghighat/sciann
Deep learning for Engineers - Physics Informed Deep Learning
jayroxis/PINNs
PyTorch Implementation of Physics-informed Neural Networks
maziarraissi/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning