Path integral derivation and numerical computation of large deviation prefactors for non-equilibrium dynamics through matrix Riccati equations

TL;DR

This paper develops a path integral method to compute sub-exponential prefactors in large deviation theory for non-equilibrium systems, involving matrix Riccati equations and numerical techniques, highlighting non-local dependencies.

Contribution

It introduces a novel derivation of large deviation prefactors for non-equilibrium dynamics using path integrals and matrix Riccati equations, emphasizing non-local effects.

Findings

Derived dynamics of Gaussian fluctuations around instantons

Presented numerical methods for computing prefactors

Highlighted non-local dependence of solutions in non-equilibrium cases

Abstract

For many non-equilibrium dynamics driven by small noise, in physics, chemistry, biology, or economy, rare events do matter. Large deviation theory then explains that the leading order term of the main statistical quantities have an exponential behavior. The exponential rate is often obtained as the infimum of an action, which is minimized along an instanton. In this paper, we consider the computation of the next order sub-exponential prefactors, which are crucial for a large number of applications. Following a path integral approach, we derive the dynamics of the Gaussian fluctuations around the instanton and compute from it the sub-exponential prefactors. As might be expected, the formalism leads to the computation of functional determinants and matrix Riccati equations. By contrast with the cases of equilibrium dynamics with detailed balance or generalized detailed balance, we stress the specific non locality of the solutions of the Riccati equation: the prefactors depend on fluctuations all along the instanton and not just at its starting and ending points. We explain how to numerically compute the prefactors. The case of statistically stationary quantities requires considerations of non trivial initial conditions for the matrix Riccati equation.