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Towards sample path estimates for fast–slow stochastic partial differential equations

  • MANUEL V. GNANN (a1), CHRISTIAN KUEHN (a1) and ANNE PEIN (a1)

Abstract

Estimates for sample paths of fast–slow stochastic ordinary differential equations have become a key mathematical tool relevant for theory and applications. In particular, there have been breakthroughs by Berglund and Gentz to prove sharp exponential error estimates. In this paper, we take the first steps in order to generalise this theory to fast–slow stochastic partial differential equations. In a simplified setting with a natural decomposition into low- and high-frequency modes, we demonstrate that for a short-time period the probability for the corresponding sample path to leave a neighbourhood around the stable slow manifold of the system is exponentially small as well.

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MVG received funding from the Deutsche Forschungsgemeinschaft (project number 334362478). CK and AP were supported by a Lichtenberg-Professorship awarded to CK. CK also acknowledges partial support via the DFG-DACH grant “Analysis of PDE with Cross-Diffusion and Stochastic Terms”. The authors would like to thank Dirk Blömker, Nils Berglund and Alexandra Neamtu for interesting discussions regarding fast–slow SPDEs and the reviewers for their helpful comments.

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Towards sample path estimates for fast–slow stochastic partial differential equations

  • MANUEL V. GNANN (a1), CHRISTIAN KUEHN (a1) and ANNE PEIN (a1)

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