For the equilibrium generalized Langevin equation with unit mass, the random force has zero mean and the displayed autocorrelation, where is the equilibrium velocity variance. If the deterministic relaxation is stable, and , then the stationary velocity autocorrelation function is , equivalently
One proof uses the power spectral density. The even force autocorrelation has spectrum . With transfer function , the velocity spectrum is , the spectrum of the even extension of . Stability removes initial transients with finite second moment. A valid force autocorrelation must have nonnegative power spectral density; an arbitrary proposed memory kernel does not automatically satisfy this condition.
A Langevin equation with a friction memory kernel, rather than only instantaneous friction. The equation couples the velocity to its earlier history through a causal convolution. Equilibrium noise and friction are related by a generalized fluctuation-dissipation relation. The name extends the Langevin equation associated with Paul Langevin.
Memory kernel 2026-10-25
A function weighting the influence of past values in a history-dependent equation. In a Volterra integral equation its convolution with the history is causal: only earlier times appear. For an integrable kernel, measures its response to a constant input. In a generalized Langevin equation, the friction memory kernel records delayed resistance to the velocity.
We use the equilibrium generalized Langevin equation and its generalized fluctuation-dissipation relation, with the factor in the noise autocorrelation fixing the equilibrium velocity variance. Let be the velocity relaxation function and let a tilde denote a one-sided Laplace transform:
The equilibrium velocity autocorrelation function is , so
For completeness, this relation follows without assuming independence of the noise and velocity in a stationary infinite history. The two-sided noise power spectral density is . The stable linear equation has velocity transfer function , so
This is precisely the power spectral density of the even extension of . Stable relaxation removes initial transients with a finite second moment, giving the long-time autocorrelation function defined in the question. All the parameter choices used below have stable relaxation.
Integrating the velocity autocorrelation function twice gives the mean squared displacement from a fixed initial position:
A decaying initial transient does not change its asymptotic slope. Therefore the Green-Kubo formula for diffusion gives
For the given friction memory kernel, direct integration yields
At , the integrated friction is , hence
The relaxation denominator here is . Its roots have negative real parts by the cubic Routh-Hurwitz criterion, since all coefficients are positive and . This justifies the decay and integrability used above. The diffusion coefficient depends on the area under the friction memory kernel, not just its value at zero.
For , , , the friction memory kernel and its Laplace transform are
Using the generalized fluctuation-dissipation relation with equilibrium velocity variance , the velocity autocorrelation function has Laplace transform
All three poles are negative, so the stationary relaxation is stable. A partial fraction decomposition gives
Taking the inverse Laplace transform gives
The negative middle coefficient is compatible with an autocorrelation function; individual terms in a partial fraction decomposition need not themselves be separate physical modes with positive weights. Several useful checks follow directly:
The Volterra integral equation for predicts exactly and . Its integral is also , agreeing with the Green-Kubo formula for diffusion.
Write , so the friction memory kernel is and has area one for every . Its Laplace transform is . The generalized fluctuation-dissipation relation therefore gives
To verify the pointwise time-domain limit, rather than relying only on Laplace transform convergence, consider . The two roots
are negative and satisfy , . Since and , the inverse Laplace transform is
The coefficient of the slow exponential tends to , and that of the fast exponential tends to zero. Hence
This is the velocity autocorrelation function of an Ornstein-Uhlenbeck process satisfying . The friction memory kernel becomes local in time. Consistently, the even noise autocorrelation tends to in the sense of distributions, fixing the white noise normalization. The finite- right derivative does not converge to the right derivative of the limiting function: there is a short initial relaxation layer.