Pith. sign in

REVIEW 2 cited by

Strong Stability Preservation for Stochastic Partial Differential Equations

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2411.11172 v2 pith:QUKI2SZJ submitted 2024-11-17 math.NA cs.NAmath.PR

James Woodfield This is my paper · ORCID
classification math.NAcs.NAmath.PR
keywords stochasticdifferentialequationsdatapartialpreservationsolutionsstability
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

This paper extends deterministic notions of Strong Stability Preservation (SSP) to the stochastic setting, enabling nonlinearly stable numerical solutions to stochastic differential equations (SDEs) and stochastic partial differential equations (SPDEs) with pathwise solutions that remain unconditionally bounded. This approach may offer modelling advantages in data assimilation, particularly when the signal or data is a realization of an SPDE or PDE with a monotonicity property.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Discontinuous Galerkin methods for the complete stochastic Euler equations

    math.NA 2024-12 conditional novelty 6.0 of 10

    An entropy-dissipative DG method for the stochastic full Euler system is shown to converge in law to a dissipative martingale solution, with error bounds up to a stopping time.

  2. Numerical comparison of energy- versus circulation-preserving stochastic vortex dynamics

    physics.flu-dyn 2026-06 unverdicted novelty 5.0 of 10

    SALT perturbations localize near vorticity gradients while SFLT produces diffuse variance across the domain in stochastic 2D Euler vortex dynamics.

Pith tools