Global solvability and convergence to stationary solutions in singular quasilinear stochastic PDEs
Tadahisa Funaki, Bin Xie

TL;DR
This paper establishes global existence and long-term convergence of solutions for certain singular quasilinear stochastic PDEs, using energy and Poincaré inequalities, and analyzes their stability and regularity properties.
Contribution
It proves global solvability and convergence to stationary solutions for a class of singular quasilinear SPDEs, extending previous results with new uniform estimates and stability analysis.
Findings
Proved global-in-time solvability of specific singular SPDEs.
Established convergence of solutions to stationary states as time approaches infinity.
Demonstrated the uniformity of Poincaré constants in approximations.
Abstract
We consider singular quasilinear stochastic partial differential equations (SPDEs) studied in \cite{FHSX}, which are defined in paracontrolled sense. The main aim of the present article is to establish the global-in-time solvability for a particular class of SPDEs with origin in particle systems and, under a certain additional condition on the noise, prove the convergence of the solutions to stationary solutions as . We apply the method of energy inequality and Poincar\'e inequality. It is essential that the Poincar\'e constant can be taken uniformly in an approximating sequence of the noise. We also use the continuity of the solutions in the enhanced noise, initial values and coefficients of the equation, which we prove in this article for general SPDEs discussed in \cite{FHSX} except that in the enhanced noise. Moreover, we apply the initial layer property of improving…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsStochastic processes and financial applications · Stability and Controllability of Differential Equations · Numerical methods in inverse problems
