Supermodeling: The Next Level of Abstraction in the Use of Data Assimilation
| Autor: | Witold Dzwinel, Gregory S. Duane, Marcin Sendera |
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| Rok vydání: | 2020 |
| Předmět: |
Scheme (programming language)
Matching (statistics) Dynamical systems theory Computer science Complex system 01 natural sciences Article Supermodeling 010305 fluids & plasmas Task (project management) 010104 statistics & probability Data assimilation Dynamical systems 0103 physical sciences Key (cryptography) 0101 mathematics computer Algorithm Abstraction (linguistics) computer.programming_language |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030504328 ICCS (6) Computational Science – ICCS 2020 |
| DOI: | 10.1007/978-3-030-50433-5_11 |
| Popis: | Data assimilation (DA) is a key procedure that synchronizes a computer model with real observations. However, in the case of overparametrized complex systems modeling, the task of parameter-estimation through data assimilation can expand exponentially. It leads to unacceptable computational overhead, substantial inaccuracies in parameter matching, and wrong predictions. Here we define a Supermodel as a kind of ensembling scheme, which consists of a few sub-models representing various instances of the baseline model. The sub-models differ in parameter sets and are synchronized through couplings between the most sensitive dynamical variables. We demonstrate that after a short pretraining of the fully parametrized small sub-model ensemble, and then training a few latent parameters of the low-parameterized Supermodel, we can outperform in efficiency and accuracy the baseline model matched to data by a classical DA procedure. |
| Databáze: | OpenAIRE |
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