Quantitative convergence rates for scaling limit of SPDEs with transport noise
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2021
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| _version_ | 1866913326358855680 |
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| author | Flandoli, Franco Galeati, Lucio Luo, Dejun |
| author_facet | Flandoli, Franco Galeati, Lucio Luo, Dejun |
| contents | We consider on the torus the scaling limit of stochastic 2D (inviscid) fluid dynamical equations with transport noise to deterministic viscous equations. Quantitative estimates on the convergence rates are provided by combining analytic and probabilistic arguments, especially heat kernel properties and maximal estimates for stochastic convolutions. Similar ideas are applied to the stochastic 2D Keller-Segel model, yielding explicit choice of noise to ensure that the blow-up probability is less than any given threshold. Our approach also gives rise to some mixing property for stochastic linear transport equations and dissipation enhancement in the viscous case. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2104_01740 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Quantitative convergence rates for scaling limit of SPDEs with transport noise Flandoli, Franco Galeati, Lucio Luo, Dejun Probability We consider on the torus the scaling limit of stochastic 2D (inviscid) fluid dynamical equations with transport noise to deterministic viscous equations. Quantitative estimates on the convergence rates are provided by combining analytic and probabilistic arguments, especially heat kernel properties and maximal estimates for stochastic convolutions. Similar ideas are applied to the stochastic 2D Keller-Segel model, yielding explicit choice of noise to ensure that the blow-up probability is less than any given threshold. Our approach also gives rise to some mixing property for stochastic linear transport equations and dissipation enhancement in the viscous case. |
| title | Quantitative convergence rates for scaling limit of SPDEs with transport noise |
| topic | Probability |
| url | https://arxiv.org/abs/2104.01740 |