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2019 | OriginalPaper | Chapter

Solving Large Dynamical Systems by Constraint Sampling

Authors : Omeiza Olumoye, Glen Throneberry, Angel Garcia, Leobardo Valera, Abdessattar Abdelkefi, Martine Ceberio

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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Abstract

The ability to conduct fast and reliable simulations of dynamic systems is of special interest to many fields of operations. Such simulations can be very complex and, to be thorough, involve millions of variables, making it prohibitive in CPU time to run repeatedly for many different configurations. Reduced-Order Modeling (ROM) provides a concrete way to handle such complex simulations using a realistic amount of resources. However, when the original dynamical system is very large, the resulting reduced-order model, although much “thinner”, is still as tall as the original system, i.e., it has the same number of equations. In some extreme cases, the number of equations is prohibitive and cannot be loaded in memory. In this work, we combine traditional interval constraint solving techniques with a strategy to reduce the number of equations to consider. We describe our approach and report preliminary promising results.

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Metadata
Title
Solving Large Dynamical Systems by Constraint Sampling
Authors
Omeiza Olumoye
Glen Throneberry
Angel Garcia
Leobardo Valera
Abdessattar Abdelkefi
Martine Ceberio
Copyright Year
2019
Book
Applied Computer Sciences in Engineering

Print ISBN: 978-3-030-31018-9

Electronic ISBN: 978-3-030-31019-6

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-31019-6_1

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