Optimal control of SPDEs driven by time-space Brownian motion

TL;DR

This paper develops a stochastic maximum principle for controlling systems governed by SPDEs driven by two-parameter Brownian motion, with applications to ecological models, control problems, and potential machine learning uses.

Contribution

It introduces a Pontryagin maximum principle for SPDEs driven by space-time Brownian motion, extending stochastic control theory to new types of infinite-dimensional systems.

Findings

Derived necessary and sufficient optimality conditions.

Solved linear quadratic and harvesting control problems.

Explored applications to machine learning.

Abstract

In this paper we study a Pontryagin type stochastic maximum principle for the optimal control of a system, where the state dynamics satisfy a stochastic partial differential equation (SPDE) driven by a two-parameter (time-space) Brownian motion (also called Brownian sheet). We first discuss some properties of a Brownian sheet driven linear SPDE which models the growth of an ecosystem. Further, applying time-space white noise calculus we derive sufficient conditions and necessary conditions of optimality of the control. Finally, we illustrate our results by solving a linear quadratic control problem and an optimal harvesting problem in the plane. We also study possible applications to machine learning.