Arbeitspapier
Surrogate Models for Optimization of Dynamical Systems
Driven by increased complexity of dynamical systems, the solution of system of differential equations through numerical simulation in optimization problems has become computationally expensive. This paper provides a smart data driven mechanism to construct low dimensional surrogate models. These surrogate models reduce the computational time for solution of the complex optimization problems by using training instances derived from the evaluations of the true objective functions. The surrogate models are constructed using combination of proper orthogonal decomposition and radial basis functions and provides system responses by simple matrix multiplication. Using relative maximum absolute error as the measure of accuracy of approximation, it is shown surrogate models with latin hypercube sampling and spline radial basis functions dominate variable order methods in computational time of optimization, while preserving the accuracy. These surrogate models also show robustness in presence of model non-linearities. Therefore, these computational efficient predictive surrogate models are applicable in various fields, specifically to solve inverse problems and optimal control problems, some examples of which are demonstrated in this paper.
- Sprache
-
Englisch
- Erschienen in
-
Series: IRTG 1792 Discussion Paper ; No. 2021-001
- Klassifikation
-
Wirtschaft
Mathematical and Quantitative Methods: General
- Thema
-
Proper Orthogonal Decomposition
SVD
Radial Basis Functions
Optimization
Surrogate Models
Smart Data Analytics
Parameter Estimation
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Khowaja, Kainat
Shcherbatyy, Mykhaylo
Härdle, Wolfgang Karl
- Ereignis
-
Veröffentlichung
- (wer)
-
Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
- (wo)
-
Berlin
- (wann)
-
2021
- Handle
- Letzte Aktualisierung
-
20.09.2024, 08:22 MESZ
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Khowaja, Kainat
- Shcherbatyy, Mykhaylo
- Härdle, Wolfgang Karl
- Humboldt-Universität zu Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series"
Entstanden
- 2021
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