SDDEs limits solutions to sublinear reaction-diffusion SPDEs
Hassan Allouba

TL;DR
This paper introduces a new SDDEs-based solution concept for heat-based SPDEs driven by white noise, proving existence, regularity, and uniqueness for reaction-diffusion SPDEs with less-than-Lipschitz coefficients, and analyzing their asymptotic behavior.
Contribution
It extends SDDEs limit solutions to reaction-diffusion SPDEs with non-Lipschitz coefficients and explores their regularity, uniqueness, and asymptotic properties.
Findings
Existence of SDDEs limit solutions under less-than-Lipschitz conditions.
Proved regularity and uniqueness in law for the solutions.
Analyzed the effect of different scaling limits on the SPDEs behavior.
Abstract
We start by introducing a new definition of solutions to heat-based SPDEs driven by space-time white noise: SDDEs (stochastic differential-difference equations) limits solutions. In contrast to the standard direct definition of SPDEs solutions; this new notion, which builds on and refines our SDDEs approach to SPDEs from earlier work, is entirely based on the approximating SDDEs. It is applicable to, and gives a multiscale view of, a variety of SPDEs. We extend this approach in related work to other heat-based SPDEs (Burgers, Allen-Cahn, and others) and to the difficult case of SPDEs with multi-dimensional spacial variable. We focus here on one-spacial-dimensional reaction-diffusion SPDEs; and we prove the existence of a SDDEs limit solution to these equations under less-than-Lipschitz conditions on the drift and the diffusion coefficients, thus extending our earlier SDDEs work to the…
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 · Stochastic processes and statistical mechanics · Mathematical Biology Tumor Growth
