Optimal perturbations for nonlinear systems using graph-based optimal transport

TL;DR

This paper introduces a graph-based optimal transport method for nonlinear systems to compute finite-time perturbations that efficiently transport measures in phase space, leveraging transfer operators and convex optimization.

Contribution

It develops a novel discrete-time, graph-based algorithm combining transfer operators and optimal mass transport for optimal perturbations in nonlinear systems.

Findings

Optimal perturbations exploit phase space structures like lobe dynamics.

The method efficiently transports measures between disjoint sets in chaotic systems.

Perturbations become more localized as the time horizon increases.

Abstract

We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.