Open Access
Entropy, volume 23, issue 1, pages 1-26
Towards generative design of computationally efficient mathematical models with evolutionary learning
Publication type: Journal Article
Publication date: 2020-12-27
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.
Citations by journals
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1
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Micromachines
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Micromachines
1 publication, 10%
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Future Generation Computer Systems
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Future Generation Computer Systems
1 publication, 10%
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Materials
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Materials
1 publication, 10%
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Mathematics
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Mathematics
1 publication, 10%
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Journal of Constructional Steel Research
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Journal of Constructional Steel Research
1 publication, 10%
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Journal of the Brazilian Society of Mechanical Sciences and Engineering
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Journal of the Brazilian Society of Mechanical Sciences and Engineering
1 publication, 10%
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Procedia Computer Science
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Procedia Computer Science
1 publication, 10%
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1
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Citations by publishers
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1
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3
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Multidisciplinary Digital Publishing Institute (MDPI)
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Multidisciplinary Digital Publishing Institute (MDPI)
3 publications, 30%
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Elsevier
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Elsevier
3 publications, 30%
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Springer Nature
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Springer Nature
1 publication, 10%
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IEEE
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IEEE
1 publication, 10%
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1
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3
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- Statistics recalculated weekly.
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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.
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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.
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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 -
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@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}
}
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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, pp. 1-26. https://doi.org/10.3390%2Fe23010028.