Skip to content
DraconiticResearch
Search Sources API
197,693 studies31 sourcesUpdated 43 min ago
PreprintarXiv licencearXiv

A Class of Stochastic Partial Differential Equations with Jumps in Fluid Dynamics: Large and Moderate Deviation Asymptotics

Abstract

We establish large and moderate deviation principles for a broad class of nonlinear stochastic partial differential equations driven by multiplicative Poisson random measures in the small-noise regime. Our abstract framework is formulated for locally monotone evolution equations over a Hilbert triple and encompasses several important models arising in fluid dynamics and turbulence theory, including the two-dimensional Navier--Stokes equations, magnetohydrodynamics (MHD), the GOY shell model of turbulence, the two-dimensional tidal equations, and Navier--Stokes equations with nonlinear viscosities. A key analytical ingredient is the well-posedness of the associated controlled equations, established using the method of local monotonicity. The large and moderate deviation principles are then proved via the weak convergence approach.

Related

  1. Long-Time Large Deviation Asymptotics for Mean-Field Granular Media Equations
    Amarjit Budhiraja, Jack Sullivan
    arXivPreprintarXiv licenceFull textProbabilityarXiv
  2. Large deviation principles for 2D stochastic g-Navier--Stokes equations on unbounded channel-like domains
    Gang He, Ji Shu, Huixia Wen +1
    arXivPreprintCC BY 4.0Full textAnalysis & PDEsProbabilityarXiv
  3. Mean field singular stochastic partial differential equations
    Ismaël Bailleul, Nicolas Moench
    Journal de l'École polytechnique — MathématiquesPeer-reviewedCC BYFull textGeneral mathematicsHAL
  4. High-order fractional linear differential equations do not describe stochastic processes with finite state space
    P. Van Mieghem
    arXivPreprintCC BY-NC-ND 4.0Full textAnalysis & PDEsDynamical systemsarXiv
  5. On the Pontryagin Maximum Principle for Rough Stochastic Differential Equations
    Peter K. Friz, Ulrich Horst, Stefanos Theodorakopoulos +1
    arXivPreprintCC BY 4.0Full textOptimisation & controlProbabilityarXiv
BibTeX
@misc{ganguly2026class,
  title = {{A Class of Stochastic Partial Differential Equations with Jumps in Fluid Dynamics: Large and Moderate Deviation Asymptotics}},
  author = {Arnab Ganguly and Padmanabhan Sundar},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.05837},
  eprint = {2610.05837},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.05837},
}
RIS
TY  - UNPB
TI  - A Class of Stochastic Partial Differential Equations with Jumps in Fluid Dynamics: Large and Moderate Deviation Asymptotics
AU  - Arnab Ganguly
AU  - Padmanabhan Sundar
PY  - 2026
DA  - 2026/10/05
PB  - arXiv
DO  - 10.48550/arxiv.2610.05837
UR  - https://arxiv.org/abs/2610.05837
AB  - We establish large and moderate deviation principles for a broad class of nonlinear stochastic partial differential equations driven by multiplicative Poisson random measures in the small-noise regime. Our abstract framework is formulated for locally monotone evolution equations over a Hilbert triple and encompasses several important models arising in fluid dynamics and turbulence theory, including the two-dimensional Navier--Stokes equations, magnetohydrodynamics (MHD), the GOY shell model of turbulence, the two-dimensional tidal equations, and Navier--Stokes equations with nonlinear viscosities. A key analytical ingredient is the well-posedness of the associated controlled equations, established using the method of local monotonicity. The large and moderate deviation principles are then proved via the weak convergence approach.
ER  -