Abstract:In this paper we develop a method to solve (stochastic) evolution equations on Gelfand triples with time-fractional derivative based on monotonicity techniques. Applications include deterministic and stochastic quasi-linear partial differential equations with time-fractional derivatives, including time-fractional (stochastic) porous media equations (including the case where the Laplace operator is also fractional) and p-Laplace equations as special cases.
“…On the other hand, some very interesting quasilinear SPDEs have been studied a lot recently, such as stochastic porous media equation and stochastic p-Laplace equation, see e.g. [9,26,27,28,29,38,39,42,43,46,49,58]) and references therein. We would like to investigate whether small time asymptotics (LDP) results also hold for those SPDE models or not?…”
Section: Shihu LI Wei Liu and Yingchao Xiementioning
confidence: 99%
“…It was first investigated in the seminal works of Pardoux [48] and Krylov and Rozovskii [34], where they adapted the monotonicity tricks to prove the existence and uniqueness of solutions for a class of SPDE. Recently, this framework has been substantially extended by the second named author and Röckner in [40,41,42,43] for more general class of SPDE with coefficients satisfying the generalized coercivity and local monotonicity conditions. In recent years, various properties for SPDEs with monotone or locally monotone coefficients has been intensively investigated in the literature, such as small noise LDP [39,45,49,59], random attractors [26,27,28,29], Harnack inequality and applications [38], Wong-Zakai approximation and support theorem [46], ultra-exponential convergence [58], and existence of optimal controls [16].…”
Section: Shihu LI Wei Liu and Yingchao Xiementioning
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 stochastic Ladyzhenskaya model. In particular, our small time LDP result seems to be new in the case of general quasilinear SPDEs with multiplicative noise.
“…On the other hand, some very interesting quasilinear SPDEs have been studied a lot recently, such as stochastic porous media equation and stochastic p-Laplace equation, see e.g. [9,26,27,28,29,38,39,42,43,46,49,58]) and references therein. We would like to investigate whether small time asymptotics (LDP) results also hold for those SPDE models or not?…”
Section: Shihu LI Wei Liu and Yingchao Xiementioning
confidence: 99%
“…It was first investigated in the seminal works of Pardoux [48] and Krylov and Rozovskii [34], where they adapted the monotonicity tricks to prove the existence and uniqueness of solutions for a class of SPDE. Recently, this framework has been substantially extended by the second named author and Röckner in [40,41,42,43] for more general class of SPDE with coefficients satisfying the generalized coercivity and local monotonicity conditions. In recent years, various properties for SPDEs with monotone or locally monotone coefficients has been intensively investigated in the literature, such as small noise LDP [39,45,49,59], random attractors [26,27,28,29], Harnack inequality and applications [38], Wong-Zakai approximation and support theorem [46], ultra-exponential convergence [58], and existence of optimal controls [16].…”
Section: Shihu LI Wei Liu and Yingchao Xiementioning
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 stochastic Ladyzhenskaya model. In particular, our small time LDP result seems to be new in the case of general quasilinear SPDEs with multiplicative noise.
“…Of importance for physics are also fractional kinetic equations with application to statistical mechanics and fractional stochastic PDEs. For these developments we refer to [37], [45] and [51], [55], [79], [80] and references therein.…”
This paper aims at unifying and clarifying the recent advances in the analysis of the fractional and generalized fractional Partial Differential Equations of Caputo and Riemann-Liouville type arising essentially from the probabilistic point of view. This point of view leads to the path integral representation for the solutions of these equations, which is seen to be stable with respect to the initial data and key parameters and is directly amenable to numeric calculations (Monte-Carlo simulation). In many cases these solutions can be compactly presented via the wide class of operator-valued analytic functions of the Mittag-Leffler type, which are proved to be expressed as the Laplace transforms of the exit times of monotone Markov processes.
“…∂ β t f (t) = f (0+) = lim t→0,t>0 f (t), seems to achieve the right balance between mathematical utility and physical relevance [8] and has been recently used in the study of large classes of stochastic partial differential equations [4,11].…”
For stochastic evolution equations with fractional derivatives, classical solutions exist when the order of the time derivative of the unknown function is not too small compared to the order of the time derivative of the noise; otherwise, there can be a generalized solution in suitable weighted chaos spaces. Presence of fractional derivatives in time leads to various modifications of the stochastic parabolicity condition. Interesting new effects appear when the order of the time derivative in the noise term is less than or equal to one-half.November 1, 20182010 Mathematics Subject Classification. Primary 60G22; Secondary 26A33, 60G20.
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