PySINDy – A package for the sparse identification of nonlinear dynamical systems

PySINDy

PySINDy is a package for system identification, primarily revolving around the method of Sparse Identification of Nonlinear Dynamical systems (SINDy) method introduced in Brunton et al. (2016a). It also includes other methods from related literature.

System identification refers to the process of using measurement data to infer the governing dynamics. Once discovered, these equations can make predictions about future states, can inform control inputs, or can enable the theoretical study using analytical techniques. The resulting models are inherently interpretable and generalizable.

First Steps

Installation

The preferred way to install is with pip or conda e.g. pip install pysindy . You may have to add the --user option. Pysindy also provides several extras, e.g. pip install pysindy[miosr] :

cvxpy Convex optimizer SR3 and subclasses miosr Branch-and-bound optimizer for L0-constraint, MIOSR sbr Bayesian regression optimizer yielding posteriors, SBR .

Example

Suppose we have measurements of the position of a particle obeying the following dynamical system at different points in time

x' &= -2 x \\ y' &= y

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