A library for solving differential equations using neural networks based on PyTorch, used by multiple research groups around the world, including at Harvard IACS.

Solving differential equations in Python using DifferentialEquations.jl and the SciML Scientific Machine Learning organization

Physics-Informed Neural networks for Advanced modeling

Code for the paper "Learning Differential Equations that are Easy to Solve"

A differentiable physics engine and multibody dynamics library for control and robot learning.

Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.

Python library for ODE integration via Taylor's method and LLVM

Operator Inference for data-driven, non-intrusive model reduction of dynamical systems.

odeintw provides a wrapper of scipy.integrate.odeint that allows it to handle complex and matrix differential equations.

A Python Framework for Modeling and Analysis of Signaling Systems

Python implementation of solvers for differential algebraic equation's (DAE's) that should be added to scipy one day.

[TGRS21] Crop Classification under Varying Cloud Cover with Neural Ordinary Differential Equations

SymDer: Symbolic Derivative Approach to Discovering Sparse Interpretable Dynamics from Partial Observations

Python toolbox to detect limit cycles and asses their stability

A Python library for solving Initial Value Problems using various numerical integration methods.

Extend scipy.integrate with various methods for solve_ivp

ODE system solver using dG(q), time-discontinuous Galerkin with Lobatto basis.

Stochastic Cellular Automata epidemic models in Python with 2D simulations

Reinforcement learning for batch bioprocess optimization (Computers & Chemical Engineering, 2020)

MOCCASIN translates basic ODE-based MATLAB models of biological processes into SBML format.