funkfuzz
"Big whirls have little whirls that feed on their velocity; little whirls have lesser whirls & so on to viscosity"
Berlin/Sofia
funkfuzz's Stars
codecrafters-io/build-your-own-x
Master programming by recreating your favorite technologies from scratch.
CorentinJ/Real-Time-Voice-Cloning
Clone a voice in 5 seconds to generate arbitrary speech in real-time
pmndrs/react-three-fiber
🇨🇭 A React renderer for Three.js
google-research/tuning_playbook
A playbook for systematically maximizing the performance of deep learning models.
visenger/awesome-mlops
A curated list of references for MLOps
humanloop/awesome-chatgpt
Curated list of awesome tools, demos, docs for ChatGPT and GPT-3
openai/point-e
Point cloud diffusion for 3D model synthesis
maziarraissi/PINNs
Physics Informed Deep Learning: Data-driven Solutions and Discovery of Nonlinear Partial Differential Equations
nalgeon/sqlean
The ultimate set of SQLite extensions
Janspiry/Image-Super-Resolution-via-Iterative-Refinement
Unofficial implementation of Image Super-Resolution via Iterative Refinement by Pytorch
lululxvi/deepxde
A library for scientific machine learning and physics-informed learning
rlguy/Blender-FLIP-Fluids
The FLIP Fluids addon is a tool that helps you set up, run, and render high quality liquid fluid effects all within Blender, the free and open source 3D creation suite.
facebookresearch/consistent_depth
We estimate dense, flicker-free, geometrically consistent depth from monocular video, for example hand-held cell phone video.
vincent-thevenin/Realistic-Neural-Talking-Head-Models
My implementation of Few-Shot Adversarial Learning of Realistic Neural Talking Head Models (Egor Zakharov et al.).
twhui/LiteFlowNet
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018 (Spotlight paper, 6.6%)
OpenWebCAD/node-occ
build BREP Solids with OpenCascade and NodeJS - 3D Modeling
benmoseley/FBPINNs
Solve forward and inverse problems related to partial differential equations using finite basis physics-informed neural networks (FBPINNs)
AndreWeiner/machine-learning-applied-to-cfd
Examples of how to use machine learning algorithms in computational fluid dynamics.
nobuyuki83/delfem2
Research prototyping framework for physics simulation written in C++
maziarraissi/HPM
Hidden physics models: Machine learning of nonlinear partial differential equations
marl/GuitarSet
GuitarSet: a dataset for guitar transcription
sseemayer/mixem
Pythonic Expectation-Maximization (EM) implementation for fitting mixtures of probability densities
KTH-Nek5000/DeepTurbulence
simsisim/CFD-DeepLearning-UNET
CFD-DeepLearning-UNET
jcallaham/robust-flow-reconstruction
Code for "Robust flow field reconstruction from limited measurements vis sparse representation" (J. Callaham, K. Maeda, and S. Brunton 2018)
RicardoMoya/ExpectationMaximization_Python
El Expectation-maximization (EM) es un método estadístico de Clustering similar al K-means, pero con un enfoque probabilístico.
torchvtk/torchvtk
PyTorch volume toolkit. Efficient data loading, dataset conversions, visualization tools
funkfuzz/GP-E
Gazepoint Estimation algorithm
funkfuzz/machine-learning-applied-to-cfd
Examples of how to use machine learning algorithms in computational fluid dynamics.
funkfuzz/PDE-FIND