Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
#计算机科学#A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods
#计算机科学#Closed-form Continuous-time Neural Networks
Pre-built implicit layer architectures with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Jupyter notebook with Pytorch implementation of Neural Ordinary Differential Equations
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, S...
The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R
#计算机科学#Arrays with arbitrarily nested named components.
18.S096 - Applications of Scientific Machine Learning
GPU-acceleration routines for DifferentialEquations.jl and the broader SciML scientific machine learning ecosystem
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Linear operators for discretizations of differential equations and scientific machine learning (SciML)
#计算机科学#Code for the paper "Learning Differential Equations that are Easy to Solve"
Repository for the Universal Differential Equations for Scientific Machine Learning paper, describing a computational basis for high performance SciML
Tensorflow implementation of Ordinary Differential Equation Solvers with full GPU support
Neural Graph Differential Equations (Neural GDEs)
#计算机科学#Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"