Write for the functional derivative of the action. Under time reversal in classical mechanics, changes sign whereas is even by assumption. Thus the forward and reversed Gaussian white noise histories associated with the same geometric path are
The Onsager--Machlup path probability therefore gives
Here the path probabilities are densities conditional on their respective initial states; they are not separately normalized bridges conditioned on both endpoints. The noise-to-path Jacobian determinant must be treated with a consistent discretization: it cancels when it is invariant under time reversal in classical mechanics. This is the usual additive-noise Langevin dynamics convention, including a constant-mass Underdamped Langevin dynamics or the time-reversal invariance of a path Jacobian in midpoint overdamped Langevin dynamics. A completely arbitrary velocity-dependent Lagrangian would require specifying that measure rather than deducing its cancellation solely from the parity of .
For an autonomous Lagrangian, the Euler-Lagrange equation uses
Apply the chain rule to the Hamiltonian along an arbitrary smooth path, without assuming the unforced Euler-Lagrange equation:
Consequently the energy balance for an autonomous Lagrangian is
With explicit time dependence, an additional enters . For ideal Gaussian white noise, the identity is understood through smooth-noise regularization or the Stratonovich chain rule. The original PDF correctly differentiates with respect to in ; the supplied TeX's derivative with respect to , its endpoint , and its ordinary derivative of are transcription errors.
At thermal equilibrium, microscopic reversibility equates the probabilities of a path and its reversed path when both include their Boltzmann distribution initial weights. Denote their endpoint states by , including velocity if needed. Since the Hamiltonian is even under time reversal in classical mechanics,
This detailed balance condition and the energy balance for an autonomous Lagrangian yield
This is the fluctuation-dissipation relation for a Langevin particle: the strength of Gaussian white noise is fixed by the damping and temperature, with the Boltzmann constant.
For an equilibrium coarse-grained variable, the unresolved microscopic states contribute entropy; their statistical weight is encoded in the Helmholtz free energy, rather than in a single microscopic energy. Relative to the same reference measure, , so microscopic reversibility becomes
This extension assumes an equilibrium coarse-grained description with reversible path statistics; externally driven dynamics need not obey this relation.
The Neumann boundary conditions give Fourier modes , including the spatially uniform mode . In the frame translating with the mean deposition height, that mode has no restoring force and obeys
Here is Brownian motion, independent of the initial height. More generally when these second moments exist. Removing the mean deposition drift does not remove the Gaussian white noise.
The Brownian zero mode of a fluctuating interface has no stationary distribution on the real height axis for . This conclusion does not depend on assuming finite variance: a stationary characteristic function would satisfy , hence vanish for every , contradicting its continuity at zero and . Any stationary joint distribution of the whole height would have a stationary zero-mode marginal, which is impossible. Thus the full unpinned height has no Boltzmann equilibrium. Pinning the mean height, or retaining only the nonzero Fourier modes, removes this obstruction. The PDF has ; the TeX's is a transcription error.
For and , each Fourier mode is an Ornstein-Uhlenbeck process with decay rate . Its explicit Ornstein-Uhlenbeck solution is
so the Itô isometry gives
To identify the overdamped Langevin dynamics, choose a friction , a harmonic oscillator potential , and force noise . The fluctuation-dissipation relation for a Langevin particle then defines . Its Boltzmann distribution is
This is the stationary spectrum of a linear fluctuating interface, also following from the equipartition theorem. The numerical prefactor uses exactly the mode-noise normalization given in the paper. The nonzero modes can therefore have a stationary Gaussian distribution even though the unpinned zero mode cannot.

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