Parallel Computing and Scientific Machine Learning (SciML): Methods and ...
An acausal modeling framework for automatically parallelized scientific ...
A PyTorch library entirely dedicated to neural differential equations, i...
Pre-built implicit layer architectures with O(1) backprop, GPUs, and sti...
Tutorials for doing scientific machine learning (SciML) and high-perform...
Jupyter notebook with Pytorch implementation of Neural Ordinary Differen...
A component of the DiffEq ecosystem for enabling sensitivity analysis fo...
18.S096 - Applications of Scientific Machine Learning
The lightweight Base library for shared types and functionality for defi...
Scientific machine learning (SciML) benchmarks, AI for science, and (dif...
Linear operators for discretizations of differential equations and scien...
Arrays with arbitrarily nested named components.
GPU-acceleration routines for DifferentialEquations.jl and the broader S...
Code for the paper "Learning Differential Equations that are Easy to Solve"
Documentation for the DiffEq differential equations and scientific machi...