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Quasi-Monte Carlo, Monte Carlo, and regularized gradient optimization methods for source characterization of atmospheric releases Sikorski, Krzysztof Addepalli, Bhagirath Pardyjak, E. R. Zhdanov, M. Atmospheric source problem Gaussian Plume Model Quasi Monte Carlo method gradient optimization 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 . Schloss Dagstuhl – Leibniz-Zentrum für Informatik Krzysztof Sikorski and Bhagirath Addepalli and E. R. Pardyjak and M. Zhdanov 2009 Is Part Of Dagstuhl Seminar Proceedings, Volume 9391, Algorithms and Complexity for Continuous Problems (2009) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagSemProc.09391.4 urn:nbn:de:0030-drops-22998 https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.09391.4 eng https://creativecommons.org/licenses/by/4.0/legalcode