Gaussian process modeling for stochastic multi-fidelity simulators, with application to fire safety

Abstract : To assess the possibility of evacuating a building in case of a fire, a standard method consists in simulating the propagation of fire, using finite difference methods and takes into account the random behavior of the fire, so that the result of a simulation is non-deterministic. The mesh fineness tunes the quality of the numerical model, and its computational cost. Depending on the mesh fineness, one simulation can last anywhere from a few minutes to several weeks. In this article, we focus on predicting the behavior of the fire simulator at fine meshes, using cheaper results, at coarser meshes. In the literature of the design and analysis of computer experiments, such a problem is referred to as multi-fidelity prediction. Our contribution is to extend to the case of stochastic simulators the Bayesian multi-fidelity model proposed by Picheny and Ginsbourger (2013) and Tuo et al. (2014).
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  • HAL Id : hal-01312988, version 1
  • ARXIV : 1605.02561

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Rémi Stroh, Julien Bect, Séverine Demeyer, Nicolas Fischer, Emmanuel Vazquez. Gaussian process modeling for stochastic multi-fidelity simulators, with application to fire safety. 48èmes Journées de Statistique de la SFdS (JdS 2016), May 2016, Montpellier, France. ⟨hal-01312988⟩

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