Physics-Informed Neural networks for Advanced modeling

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

Next generation ODE translator

A simulation modelling language

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

Python code for solving and visualizing “probability flow” Ordinary Differential Equations (ODEs)

different methods for solving several ode

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

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

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

Test Set for Initial Value Problems

Extend scipy.integrate with various methods for solve_ivp

A Python Framework for Modeling and Analysis of Signaling Systems

Bayesian models for ODE models in the Systems Biology Markup Language (SBML)

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

An approach to the modeling of linear systems and the analysis of their response to arbitrary inputs, together with data fitting models

Two simple codes to depict Chaos Theory and Butterfly Effect: exhibiting the Butterfly Effect

What's all this talk about cell cycle models?