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Large deviations for a class of semilinear stochastic partial differential equations

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  • Foondun, Mohammud
  • Setayeshgar, Leila

Abstract

We prove the large deviations principle (LDP) for the law of the solutions to a class of semilinear stochastic partial differential equations driven by multiplicative noise. Our proof is based on the weak convergence approach and significantly improves earlier methods.

Suggested Citation

  • Foondun, Mohammud & Setayeshgar, Leila, 2017. "Large deviations for a class of semilinear stochastic partial differential equations," Statistics & Probability Letters, Elsevier, vol. 121(C), pages 143-151.
    • Handle: RePEc:eee:stapro:v:121:y:2017:i:c:p:143-151
    DOI: 10.1016/j.spl.2016.10.019
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    References listed on IDEAS

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    1. Cardon-Weber, Caroline, 1999. "Large deviations for a Burgers'-type SPDE," Stochastic Processes and their Applications, Elsevier, vol. 84(1), pages 53-70, November.
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    Cited by:

    1. Salins, M., 2021. "Systems of small-noise stochastic reaction–diffusion equations satisfy a large deviations principle that is uniform over all initial data," Stochastic Processes and their Applications, Elsevier, vol. 142(C), pages 159-194.
    2. Leila Setayeshgar, 2023. "Uniform large deviations for a class of semilinear stochastic partial differential equations driven by a Brownian sheet," Partial Differential Equations and Applications, Springer, vol. 4(1), pages 1-12, February.

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