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A viscosity solution approach to the large deviation principle for stochastic convective Brinkman-Forchheimer equations

Sagar Gautam, Manil T. Mohan

TL;DR

This work addresses the large deviation principle for the stochastic convective Brinkman-Forchheimer (SCBF) equations on the torus $\mathbb{T}^d$ with small noise $1/\sqrt{n}$. It develops a viscosity-solution framework that identifies the Laplace limit as the solution to an infinite-dimensional HJB equation and then leverages exponential tightness to obtain the LDP in both the Skorohod path space and the continuous-path space. The core novelty lies in exploiting the monotone structure provided by the damping term $\beta|u|^{r-1}u$ (and the diffusion $-\mu\Delta u$) to prove a comparison principle without truncation, allowing a direct Laplace principle-based route instead of the Budhiraja–Dupuis weak convergence approach. The results yield an explicit rate function $I$ via a value-function representation of an associated deterministic control problem, connecting stochastic SCBF dynamics to infinite-dimensional optimal control and PDE techniques for rare-event analysis.

Abstract

This article develops the viscosity solution approach to the large deviation principle for the following two- and three-dimensional stochastic convective Brinkman-Forchheimer equations on the torus $\mathbb{T}^d,\ d\in\{2,3\}$ with small noise intensity: \begin{align*} \mathrm{d}\boldsymbol{u}_n+[-μΔ\boldsymbol{u}_n+ (\boldsymbol{u}_n\cdot\nabla)\boldsymbol{u}_n +α\boldsymbol{u}_n+β|\boldsymbol{u}_n|^{r-1}\boldsymbol{u}_n+\nabla p_n]\mathrm{d} t=\boldsymbol{f}\mathrm{d} t+\frac{1}{\sqrt{n}}\mathrm{Q}^{\frac12}\mathrm{d}\mathrm{W}, \ \nabla\cdot\boldsymbol{u}_n=0, \end{align*} where $μ,α,β>0$, $r\in[1,\infty)$, $\mathrm{Q}$ is a trace class operator and $\mathrm{W}$ is Hilbert-valued calendrical Wiener process. We build our analysis on the framework of Varadhan and Bryc, together with the techniques of [J. Feng et.al., Large Deviations for Stochastic Processes, American Mathematical Society (2006) vol. \textbf{131}]. By employing the techniques from the comparison principle, we identify the Laplace limit as the convergence of the viscosity solution of the associated second-order singularly perturbed Hamilton-Jacobi-Bellman equation. A key advantage of this method is that it establishes a Laplace principle without relying on additional sufficient conditions such as Bryc's theorem, which the literature commonly requires. For $r>3$ and $r=3$ with $2βμ\geq1$, we also derive the exponential moment bounds without imposing the classical orthogonality condition $((\boldsymbol{u}_n\cdot\nabla)\boldsymbol{u}_n,\mathrm{A}\boldsymbol{u}_n)=0$, where $\mathrm{A}=-Δ$, in both two-and three-dimensions. We first establish the large deviation principle in the Skorohod space. Then, by using the $\mathrm{C}-$exponential tightness, we finally establish the large deviation principle in the continuous space.

A viscosity solution approach to the large deviation principle for stochastic convective Brinkman-Forchheimer equations

TL;DR

This work addresses the large deviation principle for the stochastic convective Brinkman-Forchheimer (SCBF) equations on the torus with small noise . It develops a viscosity-solution framework that identifies the Laplace limit as the solution to an infinite-dimensional HJB equation and then leverages exponential tightness to obtain the LDP in both the Skorohod path space and the continuous-path space. The core novelty lies in exploiting the monotone structure provided by the damping term (and the diffusion ) to prove a comparison principle without truncation, allowing a direct Laplace principle-based route instead of the Budhiraja–Dupuis weak convergence approach. The results yield an explicit rate function via a value-function representation of an associated deterministic control problem, connecting stochastic SCBF dynamics to infinite-dimensional optimal control and PDE techniques for rare-event analysis.

Abstract

This article develops the viscosity solution approach to the large deviation principle for the following two- and three-dimensional stochastic convective Brinkman-Forchheimer equations on the torus with small noise intensity: \begin{align*} \mathrm{d}\boldsymbol{u}_n+[-μΔ\boldsymbol{u}_n+ (\boldsymbol{u}_n\cdot\nabla)\boldsymbol{u}_n +α\boldsymbol{u}_n+β|\boldsymbol{u}_n|^{r-1}\boldsymbol{u}_n+\nabla p_n]\mathrm{d} t=\boldsymbol{f}\mathrm{d} t+\frac{1}{\sqrt{n}}\mathrm{Q}^{\frac12}\mathrm{d}\mathrm{W}, \ \nabla\cdot\boldsymbol{u}_n=0, \end{align*} where , , is a trace class operator and is Hilbert-valued calendrical Wiener process. We build our analysis on the framework of Varadhan and Bryc, together with the techniques of [J. Feng et.al., Large Deviations for Stochastic Processes, American Mathematical Society (2006) vol. \textbf{131}]. By employing the techniques from the comparison principle, we identify the Laplace limit as the convergence of the viscosity solution of the associated second-order singularly perturbed Hamilton-Jacobi-Bellman equation. A key advantage of this method is that it establishes a Laplace principle without relying on additional sufficient conditions such as Bryc's theorem, which the literature commonly requires. For and with , we also derive the exponential moment bounds without imposing the classical orthogonality condition , where , in both two-and three-dimensions. We first establish the large deviation principle in the Skorohod space. Then, by using the exponential tightness, we finally establish the large deviation principle in the continuous space.

Paper Structure

This paper contains 42 sections, 24 theorems, 318 equations.

Key Result

Theorem 1.2

(LDP in $\mathrm{C}([0,T];\mathbb{H})$) Assume that $\mathop{\mathrm{Tr}}(\mathrm{Q})<\infty$ and $\boldsymbol{f}:[0,T]\to\mathbb{V}$ is bounded and continuous. Then, for $r>3$ and $r=3$ with $2\beta\mu\geq1$ in $d\in\{2,3\}$, the sequence of stochastic processes $\{\boldsymbol{Y}_n(\cdot)\}_{n\geq1

Theorems & Definitions (57)

  • Definition 1.1
  • Theorem 1.2
  • Remark 2.1
  • Remark 2.2
  • Remark 2.3
  • Lemma 2.4
  • Lemma 2.5
  • Remark 2.6
  • Remark 2.7: KWH
  • Definition 3.2
  • ...and 47 more