Abstract:This work aims to prove the small time large deviation principle (LDP) for a class of stochastic partial differential equations (SPDEs) with locally monotone coefficients in generalized variational framework. The main result could be applied to demonstrate the small time LDP for various quasilinear and semilinear SPDEs such as stochastic porous medium equations, stochastic p-Laplace equations, stochastic Burgers type equation, stochastic 2D Navier-Stokes equation, stochastic power law fluid equation and stocha… Show more
“…The small time asymptotics of stochastic systems has received extensive attention in recent years (see e.g., [13,17,20] and references therein). This property characterizes the asymptotical behavior of the underlying stochastic systems with the time tending to zero.…”
Section: Application To the Small Time Ldp Of Sdesmentioning
In this paper, we study the numerical approximation of the one-point large deviations rate functions of nonlinear stochastic differential equations (SDEs) with small noise. We show that the stochastic θ-method satisfies the one-point large deviations principle with a discrete rate function for sufficiently small step-size, and present a uniform error estimate between the discrete rate function and the continuous one on bounded sets in terms of step-size. It is proved that the convergence orders in the cases of multiplicative noises and additive noises are 1/2 and 1 respectively. Based on the above results, we obtain an effective approach to numerically approximating the large deviations rate functions of nonlinear SDEs with small time. To the best of our knowledge, this is the first result on the convergence rate of discrete rate functions for approximating the one-point large deviations rate functions associated with nonlinear SDEs with small noise.
“…The small time asymptotics of stochastic systems has received extensive attention in recent years (see e.g., [13,17,20] and references therein). This property characterizes the asymptotical behavior of the underlying stochastic systems with the time tending to zero.…”
Section: Application To the Small Time Ldp Of Sdesmentioning
In this paper, we study the numerical approximation of the one-point large deviations rate functions of nonlinear stochastic differential equations (SDEs) with small noise. We show that the stochastic θ-method satisfies the one-point large deviations principle with a discrete rate function for sufficiently small step-size, and present a uniform error estimate between the discrete rate function and the continuous one on bounded sets in terms of step-size. It is proved that the convergence orders in the cases of multiplicative noises and additive noises are 1/2 and 1 respectively. Based on the above results, we obtain an effective approach to numerically approximating the large deviations rate functions of nonlinear SDEs with small time. To the best of our knowledge, this is the first result on the convergence rate of discrete rate functions for approximating the one-point large deviations rate functions associated with nonlinear SDEs with small noise.
“…For the small-time LDP for infinite dimensional diffusion processes, the interested readers are referred to see [1,2,12,29,67,70], etc. The small time LDP for stochastic 2D Navier-Stokes equations, stochastic 3D tamed Navier-Stokes equations, stochastic quasi-geostrophic equations in the sub-critical case, stochastic two-dimensional non-Newtonian fluids, 3D stochastic primitive equations, SPDEs with locally monotone coefficients, stochastic convective Brinkman-Forchheimer equations, scalar stochastic conservation laws, are established in [68,56,40,36,18,38,50,19], respectively. Even though the work [38] covers the case of SPDEs with locally monotone coefficients like stochastic power law fluid equations, it won't cover the system under our consideration for arbitrary values of r (for example r > pd d−p ).…”
The Ladyzhenskaya-Smagorinsky equations model turbulence phenomena, and are given byIn this work, we consider the stochastic Ladyzhenskaya-Smagorinsky equations with the damping αu+β|u| r−2 u, for r ≥ 2 (α, β ≥ 0), subjected to multiplicative Gaussian noise. We show the local monotoincity (p ≥ d 2 + 1, r ≥ 2) as well as global monotonicity (p ≥ 2, r ≥ 4) properties of the linear and nonlinear operators, which along with an application of stochastic version of Minty-Browder technique imply the existence of a unique pathwise strong solution. Then, we discuss the small time asymptotics by studying the effect of small, highly nonlinear, unbounded drifts (small time large deviation principle) for the stochastic Ladyzhenskaya-Smagorinsky equations with damping.
“…Later on, such framework has been substantially generalized by the third named author and Röckner in [24,25,26] to more general hypothesises only fulfilling the local monotonicity and generalized coercivity, which covers several SPDEs such as the stochastic porous media equations, stochastic fast-diffusion equations, stochastic 2D Navier-Stokes equations and other hydrodynamical type models, stochastic p-Laplace equations, stochastic power law fluid equations, stochastic Ladyzhenskaya models, etc. We also refer the interested readers to [6,14,22,23,27,32,39,41,42] and reference therein for the properties of solutions associated with such framework.…”
This work is concerned with the Freidlin-Wentzell type large deviation principle for a family of multi-scale quasi-linear and semi-linear stochastic partial differential equations (SPDEs) with small multiplicative noise under the generalized variational setting, which extend several existing works to the multiscale process. Employing the weak convergence method developed by Dupuis and Ellis [7] and Khasminskii's time discretization approach [18], the Laplace principle for SPDEs will be derived, which is equivalent to the large deviation principle. In particular, in this paper, we do not assume any compactness of the embedding on the Gelfand triple we considered in order to deal with the case of bounded and unbounded domains in some concrete models. Our main results are applicable to a wide family of SPDEs such as stochastic porous media equations, stochastic fast-diffusion equations, stochastic 2D hydrodynamical type models, stochastic p-Laplace equations, stochastic power law fluid equations and stochastic Ladyzhenskaya models.
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