artiste_algorithmique

Prototype of an AI artist

artsy

aRtsy is an R package that implements algorithms for making generative art in a straightforward and standardized manner using 'ggplot2'.

ayce45

Hey, I'm Ayce!

bfm_multistate_stator_motor

Analysis of kinetic models for the Bacterial Flagella Motor stator assembly

binns

Biologically-informed neural networks

boltzmann.jl

Restricted Boltzmann Machines in Julia

cellgpu

GPU-accelerated simulations of Voronoi and vertex models of cells. Initial version published in Computer Physics Communications: https://doi.org/10.1016/j.cpc.2017.06.001

course-101-0250-00

ETH course - Solving PDEs in parallel on GPUs

curvedspacevertexmodel

An extension of the previous cellGPU package (and, currently, less well documented), that allows for simulations of 2D vertex models in curved space

data-driven-advection

data-driven-discretization-1d

Code for "Learning data-driven discretizations for partial differential equations"

deepymod

deepymod_tensorflow

This implementation of DeePyMoD is no longer maintained! We switched to a PyTorch based implementation: https://github.com/PhIMaL/DeePyMoD_torch

discovery-of-physics-from-data

Code to accompany the paper "Discovery of Physics from Data: Universal Laws and Discrepancies"

dnner

Deep Neural Networks Entropy from Replicas

dukelecture2018

Set of Lecture at Duke in 2018 by Lenka Zdeborova and Florent Krzakala "Statistical Physics For Optimization and Learning"

geopandas_brasil

Using Geopandas to Plot Brazil Maps

groove2groove

Code for "Groove2Groove: One-Shot Music Style Transfer with Supervision from Synthetic Data"

jax

Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more

kapre

kapre: Keras Audio Preprocessors

lucyhybrid

mlcourse_2019

INTRODUCTION TO MACHINE LEARNING: An introductory practical course by Florent Krzakala and Antoine Baker

multi_deepmod

neuralflow

The framework for inferring Langevin dynamics from spike data

parametric-discovery

pde-find

pde-read

pinns

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

pipline_morphogenesis_matlab

pydens

PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks