Szczegóły publikacji
Opis bibliograficzny
Supermodeling: the next level of abstraction in the use of data assimilation / Marcin Sendera, Gregory S. Duane, Witold DZIWNEL // W: Computational Science - ICCS 2020 : 20th International Conference : Amsterdam, The Netherlands, June 3–5, 2020 : proceedings, Pt. 6 / eds. Valeria V. Krzhizhanovskaya, [et al.]. — Cham : Springer Nature Switzeland, cop. 2020. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 12142. Theoretical Computer Science and General Issues ; ISSN 0302-9743). — ISBN: 978-3-030-50432-8; e-ISBN: 978-3-030-50433-5 . — S. 133–147. — Bibliogr. s. 146–147, Abstr. — Publikacja dostępna online od: 2020-06-15
Autorzy (3)
- Sendera Marcin
- Duane Gregory S.
- AGHDzwinel Witold
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 129176 |
|---|---|
| Data dodania do BaDAP | 2020-06-25 |
| Tekst źródłowy | URL |
| DOI | 10.1007/978-3-030-50433-5_11 |
| Rok publikacji | 2020 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 20th International Conference on Computational Science |
| Czasopisma/serie | Theoretical Computer Science and General Issues, Lecture Notes in Computer Science |
Abstract
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.