Hamiltonian stochastic Lie systems and applications

TL;DR

This paper refines the theory of stochastic Lie systems, especially in the Stratonovich framework, introduces new generalizations like stochastic foliated Lie systems, and explores their Hamiltonian structures with applications to biological and physical models.

Contribution

It corrects the stochastic Lie theorem in the Stratonovich approach, introduces stochastic foliated Lie systems, and develops methods for Hamiltonian stochastic Lie systems with applications.

Findings

The stochastic Lie theorem retains its classical form in Stratonovich calculus.

Stochastic Lie systems can differ significantly from classical ones in Itô calculus.

Applications include biological models, stochastic oscillators, and Lotka-Volterra systems.

Abstract

This paper provides a practical approach to stochastic Lie systems, i.e. stochastic differential equations whose general solutions can be written as a function depending only on a generic family of particular solutions and some constants related to initial conditions. We correct the stochastic Lie theorem characterising stochastic Lie systems, proving that, contrary to previous claims, it retains its classical form in the Stratonovich approach. Meanwhile, we show that the form of stochastic Lie systems may significantly differ from the classical one in the It\^{o} formalism. New generalisations of stochastic Lie systems, like the so-called stochastic foliated Lie systems, are introduced. Subsequently, we focus on stochastic Lie systems that are Hamiltonian systems relative to different geometric structures. Special attention is paid to the symplectic case. We study their stability properties and lay the foundations of a stochastic energy-momentum method. A stochastic Poisson coalgebra method is developed to derive superposition rules for Hamiltonian stochastic Lie systems. Potential applications of our results are presented for biological stochastic models, stochastic oscillators, stochastic Lotka--Volterra systems, Palomba--Goodwin models, among others. Our findings complement previous approaches by using stochastic differential equations instead of deterministic equations designed to capture some of the features of models of stochastic nature.