Papers
Topics
Authors
Recent
Assistant
AI Research Assistant
Well-researched responses based on relevant abstracts and paper content.
Custom Instructions Pro
Preferences or requirements that you'd like Emergent Mind to consider when generating responses.
Gemini 2.5 Flash
Gemini 2.5 Flash 187 tok/s
Gemini 2.5 Pro 40 tok/s Pro
GPT-5 Medium 32 tok/s Pro
GPT-5 High 25 tok/s Pro
GPT-4o 104 tok/s Pro
Kimi K2 177 tok/s Pro
GPT OSS 120B 436 tok/s Pro
Claude Sonnet 4.5 38 tok/s Pro
2000 character limit reached

A posteriori estimates for the stochastic total variation flow (2110.08002v2)

Published 15 Oct 2021 in math.NA and cs.NA

Abstract: We derive a posteriori error estimates for a fully discrete time-implicit finite element approximation of the stochastic total variaton flow (STVF) with additive space time noise. The estimates are first derived for an implementable fully discrete approximation of a regularized stochastic total variation flow. We then show that the derived a posteriori estimates remain valid for the unregularized flow up to a perturbation term that can be controlled by the regularization parameter. Based on the derived a posteriori estimates we propose a pathwise algorithm for the adaptive space-time refinement and perform numerical simulation for the regularized STVF to demonstrate the behavior of the proposed algorithm.

Citations (3)

Summary

We haven't generated a summary for this paper yet.

Lightbulb Streamline Icon: https://streamlinehq.com

Continue Learning

We haven't generated follow-up questions for this paper yet.

List To Do Tasks Checklist Streamline Icon: https://streamlinehq.com

Collections

Sign up for free to add this paper to one or more collections.