Popular GitHub Repositories for Sde

PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.

A training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included.

Parallel Computing and Scientific Machine Learning (SciML): Methods and Applications (MIT 18.337J/6.338J)

Diffusion Models in Medical Imaging (Published in Medical Image Analysis Journal)

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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 automated transformations of differential equations

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

Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.

Best Data Science, Data Analytics, AI, and SDE roadmaps. This repository is continually updated based on the top job postings on LinkedIn and Indeed in the data science and AI domain.

Contrib package for Stable-Baselines3 - Experimental reinforcement learning (RL) code

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.

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, SDEs, DDEs, DAEs, etc.

Scientific machine learning (SciML) benchmarks, AI for science, and (differential) equation solvers. Covers Julia, Python (PyTorch, Jax), MATLAB, R

Solvers for stochastic differential equations which connect with the scientific machine learning (SciML) ecosystem

Code for "Infinitely Deep Bayesian Neural Networks with Stochastic Differential Equations"

Extension functionality which uses Stan.jl, DynamicHMC.jl, and Turing.jl to estimate the parameters to differential equations and perform Bayesian probabilistic scientific machine learning

A library of useful callbacks for hybrid scientific machine learning (SciML) with augmented differential equation solvers

Python package to discover stochastic differential equations from time series data

A framework for developing multi-scale arrays for use in scientific machine learning (SciML) simulations