For a specified trajectory, the nonconserved order-parameter dynamics equation determines the noise realizationThe forward Onsager--Machlup path probability for Model A dynamics is therefore
Assume the order-parameter field is even under time-reversal symmetry and its free-energy functional is time-reversal invariant. The reversed path isIts time derivative changes sign, soFor additive Gaussian white noise, the trajectory-to-noise Jacobian is the same in the two directions. We assume it and all path-independent normalization factors are absorbed into equal constants . A time-reversal-odd order parameter would require the corresponding parity transformation as well.
Subtracting the two quadratic actions givesThe functional chain rule identifies the last integral as , so
Microscopic time-reversal invariance implies detailed balance. The equilibrium probability density of a configuration with free energy obeysThereforeComparison for arbitrary endpoint free energies yields the Model A fluctuation-dissipation relation
After the sudden parameter change, the functional derivative isEach Cartesian component of each Fourier mode consequently obeysThis is an Ornstein-Uhlenbeck process. The integrating-factor method givesBecause both coefficients are positive, every mode has positive decay rate.
Initial equilibrium at givesThe initial field and later noise are independent, so their cross terms vanish. DefineWith the supplied Gaussian white noise covariance, the noise contribution isUsing the decay rate found in part (c), it follows thatThis is the covariance interpolation in a Gaussian Model A quench: each mode forgets its initial equilibrium with relaxation time and approaches the final equilibrium variance. Larger- modes relax faster.
LetEvolution from the earlier time to the later time multiplies the earlier field by the deterministic decay factor, plus fresh noise independent of that field. The unequal-time correlation function is thereforeEquivalently,When both observation times are many relaxation times after the quench, the second term vanishes and the stationary Ornstein-Uhlenbeck process covariance remains:
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