Abstract
The supermodel synchronizes several imperfect instances of a baseline model - e.g., variously parametrized models of a complex system - into a single simulation engine with superior prediction accuracy. In this paper, we present convincing pieces of evidence in support of the hypothesis that supermodeling can be also used as a meta-procedure for fast data assimilation (DA). Thanks ago, the computational time of parameters’ estimation in multi-parameter models can be radically shortened. To this end, we compare various supermodeling approaches which employ: (1) three various training schemes, i.e., nudging, weighting and assimilation, (2) three classical data assimilation algorithms, i.e., ABC-SMC, 3DVAR, simplex method, and (3) various coupling schemes between dynamical variables of the ensembled models. We have performed extensive tests on a model of diversified cancer dynamics in the case of tumor growth, recurrence, and remission. We demonstrated that in all the configurations the supermodels are radically more efficient than single models trained by using classical DA schemes. We showed that the tightly coupled supermodel, trained by using the nudging scheme synchronizes the best, producing the efficient and the most accurate prognoses about cancer dynamics. Similarly, in the context of the application of supermodeling as the meta-algorithm for data assimilation, the classical 3DVAR algorithm appeared to be the most efficient baseline DA scheme for both the supermodel training and pre-training of the sub-models.
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Acknowledgement
The Authors are thankful for support from the National Science Centre, Poland grant no. 2016/21/B/ST6/01539 and the funds assigned to AGH University of Science and Technology by the Polish Ministry of Science and Higher Education. This research was supported in part by PLGrid Infrastructure and ACK CYFRONET Krakow. We also thank professor dr Greg Duane, University of Bergen, for his helpful comments and advice.
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Siwik, L., Łoś, M., Dzwinel, W. (2021). Supermodeling - A Meta-procedure for Data Assimilation and Parameters Estimation. In: Paszynski, M., Kranzlmüller, D., Krzhizhanovskaya, V.V., Dongarra, J.J., Sloot, P.M.A. (eds) Computational Science – ICCS 2021. ICCS 2021. Lecture Notes in Computer Science(), vol 12743. Springer, Cham. https://doi.org/10.1007/978-3-030-77964-1_28
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