dynamical-systems

Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.

Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/

A package for the sparse identification of nonlinear dynamical systems from data

A PyTorch library entirely dedicated to neural differential equations, implicit models and related numerical methods

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Solve and estimate Dynamic Stochastic General Equilibrium models (including the New York Fed DSGE)

Award winning software library for nonlinear dynamics and nonlinear timeseries analysis

Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)

A Control Systems Toolbox for Julia

Inclusive model of expression dynamics with conventional or metabolic labeling based scRNA-seq / multiomics, vector field reconstruction and differential geometry analyses

A Python Package For System Identification Using NARMAX Models

Differentiable controlled differential equation solvers for PyTorch with GPU support and memory-efficient adjoint backpropagation.

Python package for solving partial differential equations using finite differences.

Arrays with arbitrarily nested named components.

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

Nonlinear Dynamics: A concise introduction interlaced with code

Simulate dynamic systems expressed in block diagram form using Python

Neural Graph Differential Equations (Neural GDEs)