Evolution of Measures in Nonsmooth Dynamical Systems: Formalisms and Computation

TL;DR

This paper introduces three mathematical and numerical formalisms to analyze the evolution of measures in nonsmooth dynamical systems, validated through numerical examples and bounds on measure convergence.

Contribution

It presents novel formalisms for measure evolution in nonsmooth systems, including superposition, regularization, and time-stepping approaches, with quantitative bounds and validation.

Findings

Superposition principle formalism effectively describes measure disintegration.

Regularization approach provides bounds on Wasserstein metric convergence.

Time-stepping algorithm accurately models measure evolution in examples.

Abstract

This article develops mathematical formalisms and provides numerical methods for studying the evolution of measures in nonsmooth dynamical systems using the continuity equation. The nonsmooth dynamical system is described by an evolution variational inequality and we derive the continuity equation associated with this system class using three different formalisms. The first formalism consists of using the {superposition principle} to describe the continuity equation for a measure that disintegrates into a probability measure supported on the set of vector fields and another measure representing the distribution of system trajectories at each time instant. The second formalism is based on the regularization of the nonsmooth vector field and describing the measure as the limit of a sequence of measures associated with the regularization parameter. In doing so, we obtain quantitative bounds on the Wasserstein metric between measure solutions of the regularized vector field and the limiting measure associated with the nonsmooth vector field. The third formalism uses a time-stepping algorithm to model a time-discretized evolution of the measures and show that the absolutely continuous trajectories associated with the continuity equation are recovered in the limit as the sampling time goes to zero. We also validate each formalism with numerical examples. For the first formalism, we use polynomial optimization techniques and the moment-SOS hierarchy to obtain approximate moments of the measures. For the second formalism, we illustrate the bounds on the Wasserstein metric for an academic example for which the closed-form expression of the Wasserstein metric can be calculated. For the third formalism, we illustrate the time-stepping based algorithm for measure evolution on an example that shows the effect of the concentration of measures.