#Dynamical-systems
Showing 23 of 23 repositories tagged #dynamical-systems, ranked by stars
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.
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
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.
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.
Code for "Neural Controlled Differential Equations for Irregular Time Series" (Neurips 2020 Spotlight)
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.
Scripts and notebooks to accompany the book Data-Driven Methods for Dynamic Systems
This repository contains code released by DiffEqML Research
Open-source, graph-based Python code generator and analysis toolbox for dynamical systems (pre-implemented and custom models). Most pre-implemented models belong to the family of neural population models.
Code for "'Hey, that's not an ODE:' Faster ODE Adjoints via Seminorms" (ICML 2021)
AutoKoopman - automated Koopman operator methods for data-driven dynamical systems analysis and control.
Neural Laplace: Differentiable Laplace Reconstructions for modelling any time observation with O(1) complexity.
Awesome list for Neural Network Optimization methods.
Simulation and visualization of articulated rigid body systems in Julia
egtplot: A python package for 3-Strategy Evolutionary Games
A fast canonical-correlation-based search algorithm for feature selection, system identification, data pruning, etc.