An Algebraic Correspondence Between Stochastic Differential Equations and the Martin–Siggia–Rose Formalism

Abstract In the realm of complex systems, dynamics is often modeled in terms of a nonlinear, stochastic, ordinary differential equation (SDE) with either an additive or a multiplicative Gaussian white noise. In addition to a well-established collection of results proving existence and uniqueness of the solutions, it is of particular relevance the explicit computation of expectation values and correlation functions, since they encode the key physical information of the system under investigation. A pragmatically efficient way to dig out these quantities consists of the Martin–Siggia–Rose (MSR) formalism, which establishes a correspondence between a large class of SDEs and suitably constructed field theories formulated by means of a path-integral approach. Despite the effectiveness of this duality, there is no corresponding, mathematically rigorous proof of such correspondence. We address this issue using techniques proper of the algebraic approach to quantum field theories, which is known to provide a valuable framework to discuss rigorously the path-integral formulation of field theories as well as the solution theory both of ordinary and of partial, stochastic differential equations. In particular, working in this framework, we establish rigorously, albeit at the level of perturbation theory, a correspondence between correlation functions and expectation values computed either in the SDE or in the MSR formalism.

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Publication Details

Journal
Annales Henri Poincaré
Published
2025-04-23
DOI
https://doi.org/10.1007/s00023-025-01571-1
Primary Topic
Matrix Theory and Algorithms
Type
article
Field-Weighted Citation Impact
0.00

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article

An Algebraic Correspondence Between Stochastic Differential Equations and the Martin–Siggia–Rose Formalism

Alberto Bonicelli, Nicolò Drago, Claudio Dappiaggi
Annales Henri Poincaré
Matrix Theory and Algorithms
article

An Algebraic Correspondence Between Stochastic Differential Equations and the Martin–Siggia–Rose Formalism

Alberto Bonicelli, Nicolò Drago, Claudio Dappiaggi
article en

Abstract

Abstract In the realm of complex systems, dynamics is often modeled in terms of a nonlinear, stochastic, ordinary differential equation (SDE) with either an additive or a multiplicative Gaussian white noise. In addition to a well-established collection of results proving existence and uniqueness of the solutions, it is of particular relevance the explicit computation of expectation values and correlation functions, since they encode the key physical information of the system under investigation. A pragmatically efficient way to dig out these quantities consists of the Martin–Siggia–Rose (MSR) formalism, which establishes a correspondence between a large class of SDEs and suitably constructed field theories formulated by means of a path-integral approach. Despite the effectiveness of this duality, there is no corresponding, mathematically rigorous proof of such correspondence. We address this issue using techniques proper of the algebraic approach to quantum field theories, which is known to provide a valuable framework to discuss rigorously the path-integral formulation of field theories as well as the solution theory both of ordinary and of partial, stochastic differential equations. In particular, working in this framework, we establish rigorously, albeit at the level of perturbation theory, a correspondence between correlation functions and expectation values computed either in the SDE or in the MSR formalism.

Annales Henri Poincaré
University of Trento (IT), Istituto Nazionale di Alta Matematica Francesco Severi (IT), Sorbonne Université (FR), Istituto Nazionale di Fisica Nucleare, Trento Institute for Fundamental Physics And Applications (IT), Istituto Nazionale di Fisica Nucleare, Sezione di Pavia (IT), Institut Parisien de Chimie Moléculaire (FR)
Istituto Nazionale di Alta Matematica "Francesco Severi", Università degli Studi di Pavia, Gruppo Nazionale per la Fisica Matematica
Openalex Percentile: Top 91%
Matrix Theory and Algorithms
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