Dynamical System Parameter Path Optimization using Persistent Homology

TL;DR

This paper introduces a topological data analysis-based method for optimizing parameters in nonlinear dynamical systems, enabling navigation towards desired system responses by leveraging persistent homology and gradient descent.

Contribution

It presents a novel approach that uses differentiable persistence diagrams to guide parameter optimization in complex dynamical systems.

Findings

Successfully applied to various dynamical systems

Demonstrated ability to promote specific topological features

Showed effective parameter path optimization

Abstract

Nonlinear dynamical systems are complex and typically only simple systems can be analytically studied. In applications, these systems are usually defined with a set of tunable parameters and as the parameters are varied the system response undergoes significant topological changes or bifurcations. In a high dimensional parameter space, it is difficult to determine which direction to vary the system parameters to achieve a desired system response or state. In this paper, we introduce a new approach for optimally navigating a dynamical system parameter space that is rooted in topological data analysis. Specifically we use the differentiability of persistence diagrams to define a topological language for intuitively promoting or deterring different topological features in the state space response of a dynamical system and use gradient descent to optimally move from one point in the parameter space to another. The end result is a path in this space that guides the system to a set of parameters that yield the desired topological features defined by the loss function. We show a number of examples by applying the methods to different dynamical systems and scenarios to demonstrate how to promote different features and how to choose the hyperparameters to achieve different outcomes.