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Shape Functional Optimization with Restrictions Boosted with Machine Learning Techniques by M. López; J. Martínez; J.M. Matías; J. Taboada; J.A. Vilán is a Mathematics article available to read on EtoBox.

What is Shape Functional Optimization with Restrictions Boosted with Machine Learning Techniques about?

Shape optimization is a widely used technique in the design phase of a product. Current ongoing improvement policies require a product to fulfill a series of conditions from the perspective of mechanical resistance, fatigue, natural frequency, impact resistance, etc. All these conditions are translated into equality or inequality restrictions which must be satisfied during the optimization process that is necessary in order to determine the optimal shape. This article describes a new method for shape optimization that considers any regular shape as a possible shape, thereby improving on traditional methods limited to straight profiles or profiles established a priori. Our focus is based on using functional techniques and this approach is, based on representing the shape of the object by means of functions belonging to a finite-dimension functional space. In order to resolve this problem, the article proposes an optimization method that uses machine learning techniques for functional data in order to represent the perimeter of the set of feasible functions and to speed up the process of evaluating the restrictions in each iteration of the algorithm. The results demonstrate that the

Who reads Shape Functional Optimization with Restrictions Boosted with Machine Learning Techniques?

Author

M. López; J. Martínez; J.M. Matías; J. Taboada; J.A. Vilán

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0377-0427)

Published

2010

Language

EN

Field

Mathematics (Physical Sciences)