Shallow and Deep Evolutionary Neural Networks Applications in Solid Mechanics
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F24%3A00368815" target="_blank" >RIV/68407700:21220/24:00368815 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/978-981-99-9718-3" target="_blank" >https://doi.org/10.1007/978-981-99-9718-3</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-99-9718-3" target="_blank" >10.1007/978-981-99-9718-3</a>
Alternative languages
Result language
angličtina
Original language name
Shallow and Deep Evolutionary Neural Networks Applications in Solid Mechanics
Original language description
Data-driven models are now successfully used to solve complex multi-objective optimisation problems in materials mechanics. This chapter deals with the creation of surrogate models and its subsequent application for opti-misation by evolution-based algorithms. Models representing curved beams or frames fabricated from composite tubes with circular cross sections are discussed here. Some learning strategies based on Evolutionary Neural Net-work (EvoNN), Bi-objective Genetic Programming (BioGP), and Evolutionary Deep Neural Net (EvoDN2) algorithms were applied. The surrogate models obtained from these algorithms were subsequently subjected to multi-optimisation for computing the Pareto fronts as the resulting output. Simple geometries could be very efficiently trained and optimised by each of the above-mentioned approaches, but more complex configurations (e.g., curved beams and frames made up of more tubes or tubes with additional layers) were effectively solved only by the EvoDN2 algorithm, as demonstrated in examples.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
20302 - Applied mechanics
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Advanced Machine Learning with Evolutionary and Metaheuristic Techniques
ISBN
978-981-99-9717-6
Number of pages of the result
40
Pages from-to
257-296
Number of pages of the book
366
Publisher name
Springer Nature Singapore Pte Ltd.
Place of publication
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UT code for WoS chapter
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