A Bayesian approach of the Source Term Estimate coupling retro-dispersion computations with a Lagrangian Particle Dispersion Model and the Adaptive Multiple Importance Sampling

Abstract : This paper presents an enhanced version of a STE adaptive algorithm based on probabilistic Bayesian inference (AMIS) that estimates the parameters of an atmospheric pollution source term. After introducing the problem and presenting the initial results obtained with the first version of the algorithm, we describe an efficient way to reduce the computational load in its procedure by shifting the most time-consuming step outside the iterative loop. This is made possible by applying the duality relationship between forward and adjoint advection-diffusion equations, and is proven to work well on a synthetic example using Retro-SPRAY, the backward implementation of SPRAY, the Lagrangian particle dispersion model of the PMSS suite.
Type de document :
Communication dans un congrès
17th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (HARMO 17), May 2016, Budapest, Hungary. pp.1-5, 2016
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Contributeur : François Septier <>
Soumis le : jeudi 1 septembre 2016 - 17:41:45
Dernière modification le : jeudi 11 janvier 2018 - 06:27:22

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  • HAL Id : hal-01359091, version 1

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Harizo Rajaona, François Septier, Yves Delignon, Patrick Armand, Christophe Olry, et al.. A Bayesian approach of the Source Term Estimate coupling retro-dispersion computations with a Lagrangian Particle Dispersion Model and the Adaptive Multiple Importance Sampling. 17th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes (HARMO 17), May 2016, Budapest, Hungary. pp.1-5, 2016. 〈hal-01359091〉

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