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Open access
Entropy, volume 23, issue 1, pages 1-26

Towards generative design of computationally efficient mathematical models with evolutionary learning

Publication typeJournal Article
Publication date2020-12-27
Journal: Entropy
Quartile SCImago
Q2
Quartile WOS
Q2
Impact factor2.7
ISSN10994300
PubMed ID:  33375471
General Physics and Astronomy
Abstract

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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Citations by publishers

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Multidisciplinary Digital Publishing Institute (MDPI)
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GOST Copy
Kalyuzhnaya A. V. et al. Towards generative design of computationally efficient mathematical models with evolutionary learning // Entropy. 2020. Vol. 23. No. 1. pp. 1-26.
GOST all authors (up to 50) Copy
Kalyuzhnaya A. V., Nikitin N. O., Hvatov A., Maslyaev M., Yachmenkov M., Boukhanovsky A. Towards generative design of computationally efficient mathematical models with evolutionary learning // Entropy. 2020. Vol. 23. No. 1. pp. 1-26.
RIS |
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RIS Copy
TY - JOUR
DO - 10.3390/e23010028
UR - https://doi.org/10.3390%2Fe23010028
TI - Towards generative design of computationally efficient mathematical models with evolutionary learning
T2 - Entropy
AU - Nikitin, Nikolay O
AU - Hvatov, Alexander
AU - Maslyaev, Mikhail
AU - Yachmenkov, Mikhail
AU - Boukhanovsky, Alexander
AU - Kalyuzhnaya, Anna V
PY - 2020
DA - 2020/12/27 00:00:00
PB - Multidisciplinary Digital Publishing Institute (MDPI)
SP - 1-26
IS - 1
VL - 23
PMID - 33375471
SN - 1099-4300
ER -
BibTex |
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BibTex Copy
@article{2020_Kalyuzhnaya,
author = {Nikolay O Nikitin and Alexander Hvatov and Mikhail Maslyaev and Mikhail Yachmenkov and Alexander Boukhanovsky and Anna V Kalyuzhnaya},
title = {Towards generative design of computationally efficient mathematical models with evolutionary learning},
journal = {Entropy},
year = {2020},
volume = {23},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
month = {dec},
url = {https://doi.org/10.3390%2Fe23010028},
number = {1},
pages = {1--26},
doi = {10.3390/e23010028}
}
MLA
Cite this
MLA Copy
Kalyuzhnaya, Anna V., et al. “Towards generative design of computationally efficient mathematical models with evolutionary learning.” Entropy, vol. 23, no. 1, Dec. 2020, pp. 1-26. https://doi.org/10.3390%2Fe23010028.
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