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Using the optimal fluctuation method, we evaluate the short-time probability distribution P ( H ˉ , L , t = T ) of the spatially averaged height H ˉ = ( 1 / L ) ∫ 0 L h ( x , t = T ) d x of a one-dimensional interface h ( x , t ) governed by the Kardar–Parisi–Zhang equation ∂ t h = ν ∂ x 2 h + λ 2 ∂ x h 2 + D ξ x , t on a ring of length L. The process starts from a flat interface, h ( x , t = 0 ) = 0 . Both at λ H ˉ < 0 and at sufficiently small positive λ H ˉ the optimal (that is, the least-action) path h ( x , t ) of the interface, conditioned on H ˉ , is uniform in space, and the distribution P ( H ˉ , L , T ) is Gaussian. However, at sufficiently large λ H ˉ > 0 the spatially uniform solution becomes sub-optimal and gives way to non-uniform optimal paths. We study these, and the resulting non-Gaussian distribution P ( H ˉ , L , T ) , analytically and numerically. The loss of optimality of the uniform solution occurs via a dynamical phase transition of either first or second order, depending on the rescaled system size ℓ = L / ν T , at a critical value H ˉ = H ˉ c ( ℓ ) . At large but finite ℓ the transition is of first order. Remarkably, it becomes an ‘accidental’ second-order transition in the limit of ℓ → ∞ , where a large-deviation behavior − ln P ( H ¯ , L , T ) ≃ ( L / T ) f ( H ¯ ) (in the units λ = ν = D = 1 ) is observed. At small ℓ the transition is of second order, while at ℓ = O ( 1 ) transitions of both types occur.
Using the optimal fluctuation method, we evaluate the short-time probability distribution P ( H ˉ , L , t = T ) of the spatially averaged height H ˉ = ( 1 / L ) ∫ 0 L h ( x , t = T ) d x of a one-dimensional interface h ( x , t ) governed by the Kardar–Parisi–Zhang equation ∂ t h = ν ∂ x 2 h + λ 2 ∂ x h 2 + D ξ x , t on a ring of length L. The process starts from a flat interface, h ( x , t = 0 ) = 0 . Both at λ H ˉ < 0 and at sufficiently small positive λ H ˉ the optimal (that is, the least-action) path h ( x , t ) of the interface, conditioned on H ˉ , is uniform in space, and the distribution P ( H ˉ , L , T ) is Gaussian. However, at sufficiently large λ H ˉ > 0 the spatially uniform solution becomes sub-optimal and gives way to non-uniform optimal paths. We study these, and the resulting non-Gaussian distribution P ( H ˉ , L , T ) , analytically and numerically. The loss of optimality of the uniform solution occurs via a dynamical phase transition of either first or second order, depending on the rescaled system size ℓ = L / ν T , at a critical value H ˉ = H ˉ c ( ℓ ) . At large but finite ℓ the transition is of first order. Remarkably, it becomes an ‘accidental’ second-order transition in the limit of ℓ → ∞ , where a large-deviation behavior − ln P ( H ¯ , L , T ) ≃ ( L / T ) f ( H ¯ ) (in the units λ = ν = D = 1 ) is observed. At small ℓ the transition is of second order, while at ℓ = O ( 1 ) transitions of both types occur.
We introduce and compare computational techniques for sharp extreme event probability estimates in stochastic differential equations with small additive Gaussian noise. In particular, we focus on strategies that are scalable, i.e. their efficiency does not degrade upon temporal and possibly spatial refinement. For that purpose, we extend algorithms based on the Laplace method for estimating the probability of an extreme event to infinite dimensional path space. The method estimates the limiting exponential scaling using a single realization of the random variable, the large deviation minimizer. Finding this minimizer amounts to solving an optimization problem governed by a differential equation. The probability estimate becomes sharp when it additionally includes prefactor information, which necessitates computing the determinant of a second derivative operator to evaluate a Gaussian integral around the minimizer. We present an approach in infinite dimensions based on Fredholm determinants, and develop numerical algorithms to compute these determinants efficiently for the high-dimensional systems that arise upon discretization. We also give an interpretation of this approach using Gaussian process covariances and transition tubes. An example model problem, for which we provide an open-source python implementation, is used throughout the paper to illustrate all methods discussed. To study the performance of the methods, we consider examples of stochastic differential and stochastic partial differential equations, including the randomly forced incompressible three-dimensional Navier–Stokes equations.
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