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Towards generative design of computationally efficient mathematical models with evolutionary learning

Kalyuzhnaya A.V., Nikitin N.O., Hvatov A., Maslyaev M., Yachmenkov M., Boukhanovsky A.
Тип документаJournal Article
Дата публикации2021-01-01
Название журналаEntropy
ИздательMultidisciplinary Digital Publishing Institute (MDPI)
Квартиль по SCImagoQ2
Квартиль по Web of ScienceQ2
Импакт-фактор 20212.74
ISSN10994300
General Physics and Astronomy
Краткое описание
In this paper, we describe the concept of generative design approach applied to the automated evolutionary learning of mathematical models in a computationally efficient way. To formalize the problems of models’ design and co-design, the generalized formulation of the modeling workflow is proposed. A parallelized evolutionary learning approach for the identification of model structure is described for the equation-based model and composite machine learning models. Moreover, the involvement of the performance models in the design process is analyzed. A set of experiments with various models and computational resources is conducted to verify different aspects of the proposed approach.
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ГОСТ |
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1. Kalyuzhnaya A. V. и др. Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning // Entropy. 2020. Т. 23. № 1. С. 28.
RIS |
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TY - JOUR

DO - 10.3390/e23010028

UR - http://dx.doi.org/10.3390/e23010028

TI - Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

T2 - Entropy

AU - Kalyuzhnaya, Anna V.

AU - Nikitin, Nikolay O.

AU - Hvatov, Alexander

AU - Maslyaev, Mikhail

AU - Yachmenkov, Mikhail

AU - Boukhanovsky, Alexander

PY - 2020

DA - 2020/12/27

PB - MDPI AG

SP - 28

IS - 1

VL - 23

SN - 1099-4300

ER -

BibTex |
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@article{Kalyuzhnaya_2020,

doi = {10.3390/e23010028},

url = {https://doi.org/10.3390%2Fe23010028},

year = 2020,

month = {dec},

publisher = {{MDPI} {AG}},

volume = {23},

number = {1},

pages = {28},

author = {Anna V. Kalyuzhnaya and Nikolay O. Nikitin and Alexander Hvatov and Mikhail Maslyaev and Mikhail Yachmenkov and Alexander Boukhanovsky},

title = {Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning},

journal = {Entropy}

}

MLA
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Kalyuzhnaya, Anna V., et al. “Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning.” Entropy, vol. 23, no. 1, Dec. 2020, p. 28. Crossref, https://doi.org/10.3390/e23010028.