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An algebraic correspondence between stochastic differential equations and the Martin-Siggia-Rose formalism
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In the realm of complex systems, dynamics is often modeled in terms of a non-linear, 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.
Forward citations
Cited by 2 Pith papers
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Backward error analysis for matrix discretizations of 2-D Euler equations
ISOSYRK methods on Zeitlin’s Euler–Zeitlin system admit n-independent exponentially small modified-Hamiltonian errors for times exp(c/ε) when h = ε ℏ_n.
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Supersymmetry and Nonreciprocity
An explicit N=1 supersymmetric action exists for nonreciprocal stochastic processes: re-representing the MSR functional determinant via a Pfaffian identity yields a manifest supercharge whose square is the Hamiltonian.
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