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Erschienen in:
A High-Performance Implementation of Bayesian Matrix Factorization with Limited Communication Kapitel lesen Erstes Kapitel lesen

2020 | OriginalPaper | Buchkapitel

Supermodeling: The Next Level of Abstraction in the Use of Data Assimilation

verfasst von : Marcin Sendera, Gregory S. Duane, Witold Dzwinel

Erschienen in: Computational Science – ICCS 2020

Verlag: Springer International Publishing

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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.

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Vorheriges Kapitel Learning Ergodic Averages in Chaotic Systems
Nächstes Kapitel A New Multilayer Network Construction via Tensor Learning
Fußnoten
1
See the Chaos Focus Issue introduced in [11] for the origin and history of supermodeling.
 
2
Kalman filtering is equivalent to the popular 4D-Var algorithm, for a perfect model.
 
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Metadaten
Titel
Supermodeling: The Next Level of Abstraction in the Use of Data Assimilation
verfasst von
Marcin Sendera
Gregory S. Duane
Witold Dzwinel
Copyright-Jahr
2020
Buch
Computational Science – ICCS 2020

Print ISBN: 978-3-030-50432-8

Electronic ISBN: 978-3-030-50433-5

Copyright-Jahr: 2020

DOI
https://doi.org/10.1007/978-3-030-50433-5_11