DSINDy: Derivative-based sparse identification of nonlinear dynamics

This repository contains the code used to run DSINDy and other algorithms as described in:

Wentz, Jacqueline, and Alireza Doostan. "Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data." arXiv preprint arXiv:2211.05918 (2022).

Setting up environment

This code uses the packages specified in environment.yml and the environment can be set up using conda as follows:

conda env create -f environment.yml
conda activate dsindy-env

Note that within the environment.yml, the user must specify the path to the DSINDy package.

A mosek license is required to run the code. A personal academic license can be obtained from https://www.mosek.com/products/academic-licenses/. I place the license in the '/home/jacqui/mosek/' directory.

Example script

An example of running DSINDy is given in the notebook notebooks/example_run_of_DSINDy.py.