A matrix-noise Jacobian J_σ = ∂_j σ_ik ∂_i σ_jk − (∂_i σ_ik)(∂_l σ_lk) survives scalar cancellations and measurably affects path likelihoods and Onsager-Machlup paths in multidimensional systems.
State-dependent diffusion: thermodynamic consistency and its path integral formulation
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abstract
The friction coefficient of a particle can depend on its position as it does when the particle is near a wall. We formulate the dynamics of particles with such state-dependent friction coefficients in terms of a general Langevin equation with multiplicative noise, whose evaluation requires the introduction of specific rules. Two common conventions, the Ito and the Stratonovich, provide alternative rules for evaluation of the noise, but other conventions are possible. We show the requirement that a particle's distribution function approach the Boltzmann distribution at long times dictates that a drift term must be added to the Langevin equation. This drift term is proportional to the derivative of the diffusion coefficient times a factor that depends on the convention used to define the multiplicative noise. We explore the consequences of this result in a number examples with spatially varying diffusion coefficients. We also derive path integral representations for arbitrary interpretation of the noise, and use it in a perturbative study of correlations in a simple system.
fields
cond-mat.stat-mech 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Matrix-noise Jacobians in stochastic-calculus inference and optimal paths
A matrix-noise Jacobian J_σ = ∂_j σ_ik ∂_i σ_jk − (∂_i σ_ik)(∂_l σ_lk) survives scalar cancellations and measurably affects path likelihoods and Onsager-Machlup paths in multidimensional systems.