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

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

pyMOR - Model Order Reduction with Python

A Python native dynamical system simulation framework in the block diagram paradigm.

A library for working with Cellular Automata, for Python.

A Python module implementing some standard algorithms used in nonlinear time series analysis

Solve nonlinear heterogeneous agent models

A modular and scalable simulation framework for ultra-rapid prototyping of self-adaptive, stochastic and robust Nonlinear Model Predictive Control for Autonomous Vehicle Motion Control

A toolbox for trajectory optimization of dynamical systems

A basic nonlinear model predictive control implementation using Casadi with Unscented Kalman filter state estimation

AI4Science: Python/Matlab implementation of online and window dynamic mode decomposition (Online DMD and Window DMD)

PDE-VAE: Variational Autoencoder for Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning

Multiscale Modelling Tool - mathematical modelling without the maths

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

MIT Spring 2020 Mechanical Engineering 2.152 Nonlinear Control (Professor Jean-Jacques Slotine)

Constructing linearizing transformations for reduced-order modeling of nonlinear dynamical systems

JAX compilation of RDDL description files, and a differentiable planner in JAX.

Uncertainty sets for nonlinear dynamical systems

Published in Nature Communications: Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations