| Autor: |
Yepes Llorente, Luis, Morlier, Joseph, Sridhara, Saketh, Suresh, Krishnan |
| Předmět: |
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| Zdroj: |
Structural & Multidisciplinary Optimization; May2024, Vol. 67 Issue 5, p1-23, 23p |
| Abstrakt: |
In an increasingly competitive and digital industrial environment, the optimization of structures is a key point not only to reduce costs but also to reduce the consumption of natural resources. To this end, different approaches have emerged throughout history based on the tools available at the time. With the current rise of artificial intelligence and the concept of machine learning, revolutionary ideas are emerging that allow an optimal dimensioning of structures in record time. This work presents the use of variational autoencoders and mixed-variable solvers as a proposal for structural optimization and material selection. It has expanded upon previous research by advancing in three directions: (1) incorporating more material attributes, particularly relevant for environmental considerations; (2) analyzing in more detail aspects of VAEs such as the dimensionality of the latent space; and (3) a two-step hybrid approach to select the optimal candidate: preliminary filtering with VAE and final design via mixed-variable model. Various examples demonstrate the applicability of the proposed method. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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