Quasi-Monte Carlo, Monte Carlo, and regularized gradient optimization methods for source characterization of atmospheric releases

2009/01/01 by Krzysztof A. Sikorski, Bhagirath Addepalli, Sikorski, Krzysztof +5
Decision Sciences · Mathematics · Physics and Astronomy · #Atmospheric source problem #Gaussian Plume Model #Numerical methods in inverse problems #Probabilistic and Robust Engineering Design #Quasi Monte Carlo method #Scientific Research and Discoveries #gradient optimization

paper · doi:10.4230/dagsemproc.09391.4

openalex publication_date 2009/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

An inversion technique based on MC/QMC search and regularized gradient optimization was developed to solve the atmospheric source characterization problem. The Gaussian Plume Model was adopted as the forward operator and QMC/MC search was implemented in order to find good starting points for the gradient optimization. This approach was validated on the Copenhagen Tracer Experiments. The QMC approach with the utilization of clasical and scrambled Halton, Hammersley and Sobol points was shown to be 10-100 times more efficient than the Mersenne Twister Monte Carlo generator. Further experiments are needed for different data sets. Computational complexity analysis needs to be carried out .

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