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Towards Generative Design of Computationally Efficient Mathematical Models with Evolutionary Learning

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....

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Detalles Bibliográficos
Autores principales: Kalyuzhnaya, Anna V., Nikitin, Nikolay O., Hvatov, Alexander, Maslyaev, Mikhail, Yachmenkov, Mikhail, Boukhanovsky, Alexander
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7823403/
https://www.ncbi.nlm.nih.gov/pubmed/33375471
http://dx.doi.org/10.3390/e23010028
Descripción
Sumario: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.