Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equation...
Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and ...
#计算机科学#Physics-Informed Neural Networks (PINN) Solvers of (Partial) Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
#计算机科学#PDEBench: An Extensive Benchmark for Scientific Machine Learning
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable inte...
Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
#计算机科学#Distributed High-Performance Symbolic Regression in Julia
Code accompanying my blog post: So, what is a physics-informed neural network?
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning ...
Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software.
Data driven modeling and automated discovery of dynamical systems for the SciML Scientific Machine Learning organization
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...
Surrogate modeling and optimization for scientific machine learning (SciML)
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
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