Abstract
Reasonably recently, a new efficient method appeared for solving complex non-linear differential equations (and systems of differential equations). In this method—known as Model Order Reduction (MOR)—we select several solutions, and approximate a general solution by a linear combination of the selected solutions. In this paper, we use the known explanation for efficiency of neural networks to explain the efficiency of MOR techniques.
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References
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Acknowledgments
This work was supported in part by the National Science Foundation grants:
\(\bullet \) 1623190 (A Model of Change for Preparing a New Generation for Professional Practice in Computer Science), and
\(\bullet \) HRD-1834620 and HRD-2034030 (CAHSI Includes).
It was also supported:
\(\bullet \) by the AT &T Fellowship in Information Technology, and
\(\bullet \) by the program of the development of the Scientific-Educational Mathematical Center of Volga Federal District No. 075-02-2020-1478.
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Robles, S., Ceberio, M., Kreinovich, V. (2023). Why Model Order Reduction. In: Ceberio, M., Kreinovich, V. (eds) Decision Making Under Uncertainty and Constraints. Studies in Systems, Decision and Control, vol 217. Springer, Cham. https://doi.org/10.1007/978-3-031-16415-6_35
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DOI: https://doi.org/10.1007/978-3-031-16415-6_35
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