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arxiv: 1801.09164 · v2 · pith:ZOMCMHJ7new · submitted 2018-01-28 · 🧮 math.PR · math-ph· math.MP

Another look into the Wong-Zakai Theorem for Stochastic Heat Equation

classification 🧮 math.PR math-phmath.MP
keywords varepsilonequationheatstochasticalignanotherconvergesbegin
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Consider the heat equation driven by a smooth, Gaussian random potential: \begin{align*} \partial_t u_{\varepsilon}=\tfrac12\Delta u_{\varepsilon}+u_{\varepsilon}(\xi_{\varepsilon}-c_{\varepsilon}), \ \ t>0, x\in\mathbb{R}, \end{align*} where $\xi_{\varepsilon}$ converges to a spacetime white noise, and $c_{\varepsilon} $ is a diverging constant chosen properly. For any $ n\geq 1 $, we prove that $ u_{\varepsilon} $ converges in $ L^n $ to the solution of the stochastic heat equation. Our proof is probabilistic, hence provides another perspective of the general result of Hairer and Pardoux \cite{Hairer15a}, for the special case of the stochastic heat equation. We also discuss the transition from homogenization to stochasticity.

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