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arXiv 2306.13402math.PR
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无穷维非线性退化随机微分方程乘性噪声的次协调性

Hypocoercivity for infinite-dimensional non-linear degenerate stochastic differential equations with multiplicative noise

Alexander Bertram, Benedikt Eisenhuth, Martin Grothaus

AI总结:

本文研究无穷维非线性退化随机微分方程,建立生成算子的耗散性并构造解过程,应用次协调性方法证明指数收敛,推广至反应-扩散及Cahn-Hilliard型方程。

摘要:

我们分析具有乘性噪声的无穷维非线性退化随机微分方程。首先,在光滑有限基函数上定义的 $L^2(μ^Φ)$ 中,建立了其关联的 Kolmogorov 后向生成算子的本质 m-耗散性。这里 $Φ$ 是一个适当的势,$μ^Φ$ 是相对于无穷维非退化高斯测度具有密度 $e^{-Φ}$ 的不变测度。其次,我们使用预解方法构造具有无限寿命的相应右过程,解决 Kolmogorov 后向生成算子的鞅问题。它们为非线性退化随机偏微分方程提供具有弱连续路径的弱解。此外,我们将此类过程的转移半群与由相应 Kolmogorov 后向生成算子生成的强连续压缩半群 $(T_t)_{t\geq 0}$ 等同。随后,我们应用由 Dolbeaut、Mouhot 和 Schmeiser 发展的抽象 Hilbert 空间次协调性方法的改进,推导 $(T_t)_{t\geq 0}$ 的次协调性。即我们考虑定义域问题,并使用 Grothaus 和 Stilgenbauer 在 Kolmogorov 后向设置中阐述的表述。该方法使我们能够显式计算决定 $(T_t)_{t\geq 0}$ 平衡指数收敛速率的常数。$(T_t)_{t\geq 0}$ 与过程转移半群的等同产生后者的指数遍历性。最后,我们将结果应用于具有乘性噪声的时间二阶随机反应-扩散和 Cahn-Hilliard 型方程。更一般地,我们分析无穷维确定性微分方程与随机微分方程的相应耦合。

英文摘要:

We analyze infinite-dimensional non-linear degenerate stochastic differential equations with multiplicative noise. First, essential m-dissipativity of their associated Kolmogorov backward generators on $L^2(μ^Φ)$ defined on smooth finitely based functions is established. Here $Φ$ is an appropriate potential and $μ^Φ$ is the invariant measure with density $e^{-Φ}$ w.r.t. an infinite-dimensional non-degenerate Gaussian measure. Second, we use resolvent methods to construct corresponding right processes with infinite lifetime, solving the martingale problem for the Kolmogorov backward generators. They provide weak solutions, with weakly continuous paths, to the non-linear degenerate stochastic partial differential equations. Moreover, we identify the transition semigroup of such a process with the strongly continuous contraction semigroup $(T_t)_{t\geq 0}$ generated by the corresponding Kolmogorov backwards generator. Afterwards, we apply a refinement of the abstract Hilbert space hypocoercivity method, developed by Dolbeaut, Mouhot and Schmeiser, to derive hypocoercivity of $(T_t)_{t\geq 0}$. I.e. we take domain issues into account and use the formulation in the Kolmogorov backwards setting worked out by Grothaus and Stilgenbauer. The method enables us to explicitly compute the constants determining the exponential convergence rate to equilibrium of $(T_t)_{t\geq 0}$. The identification between $(T_t)_{t\geq 0}$ and the transition semigroup of the process yields exponential ergodicity of the latter. Finally, we apply our results to second order in time stochastic reaction-diffusion and Cahn-Hilliard type equations with multiplicative noise. More generally, we analyze corresponding couplings of infinite-dimensional deterministic with stochastic differential equations.

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