ode

Physics-Informed Neural networks for Advanced modeling

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

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

🏆 A weekly updated ranked list of popular open-source libraries and tools for Power System Analysis.

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

High-performance sensitivity analysis for large ordinary differential equation models

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

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

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

[EMNLP 2023] Composable text generation & editing by ODE sampling in the latent space of a pretrained-LM VAE — compose sentiment, tense, formality, keyword controls without retraining

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

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

HazeFlow: Revisit Haze Physical Model as ODE and Non-Homogeneous Haze Generation for Real-World Dehazing [ICCV 2025]

A Python Framework for Modeling and Analysis of Signaling Systems

Python bindings to SUNDIALS differential algebraic equation solvers

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

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

Extend scipy.integrate with various methods for solve_ivp

Python toolbox to detect limit cycles and asses their stability