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Global well-posedness to stochastic reaction-diffusion equations on the real line $\mathbb{R}$ with superlinear drifts driven by multiplicative space-time white noise

Probability 2021-06-08 v1

Abstract

Consider the stochastic reaction-diffusion equation with logarithmic nonlinearity driven by space-time white noise: \begin{align}\label{1.a} \left\{ \begin{aligned} & \mathrm{d}u(t,x) = \frac{1}{2}\Delta u(t,x)\,\mathrm{d}t+ b(u(t,x)) \,\mathrm{d}t \nonumber\\ & ~~~~~~~~~~~~~~~~ + \sigma(u(t,x)) \,W(\mathrm{d}t,\mathrm{d}x), \ t>0, x\in I , \\ & u(0,x)=u_0(x), \quad x\in I .\nonumber \end{aligned} \right. \end{align} When II is a compact interval, say I=[0,1]I=[0,1], the well-posedness of the above equation was established in [DKZ] (Ann. Prob. 47:1,2019). The case where I=RI=\mathbb{R} was left open. The essential obstacle is caused by the explosion of the supremum norm of the solution, supxRu(t,x)=\sup_{x\in\mathbb{R}}|u(t,x)|=\infty, making the usual truncation procedure invalid. In this paper, we prove that there exists a unique global solution to the stochastic reaction-diffusion equation on the whole real line R\mathbb{R} with logarithmic nonlinearity. Because of the nature of the nonlinearity, to get the uniqueness, we are forced to work with the first order moment of the solutions on the space Ctem(R)C_{tem}(\mathbb{R}) with a specially designed norm suptT,xR(u(t,x)eλxeβt),\sup_{t\leq T, x\in\mathbb{R}}\left(|u(t,x)|e^{-\lambda |x|e^{\beta t}}\right), where, unlike the usual norm in Ctem(R)C_{tem}(\mathbb{R}), the exponent also depends on time tt in a particular way. Our approach depends heavily on the new, precise lower order moment estimates of the stochastic convolution and a new type of Gronwall's inequalities we obtained, which are of interest on their own right.

Keywords

Cite

@article{arxiv.2106.02879,
  title  = {Global well-posedness to stochastic reaction-diffusion equations on the real line $\mathbb{R}$ with superlinear drifts driven by multiplicative space-time white noise},
  author = {Shijie Shang and Tusheng Zhang},
  journal= {arXiv preprint arXiv:2106.02879},
  year   = {2021}
}

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30 pages