Название: Optimization Methods for Finite Element Analysis and Design
Автор: Ishaan R. Kale, Sujin Bureerat , Ravipudi Venkata Rao
Издательство: CRC Press
Серия: Advances in Metaheuristics
Год: 2026
Страниц: 192
Язык: английский
Формат: pdf (true), epub (true)
Размер: 25.4 MB

Optimization Methods for Finite Element Analysis and Design describes recent developments in Finite Element Methods (FEM). It gives a brief introduction of the applications of AI-based nature-inspired metaheuristic algorithms and Machine Learning (ML) at various stages of FEM. The book covers a range of state-of-the-art application areas including medical equipment, structural analysis and machinery products.

It explores the applications of optimization and ML techniques in mesh smoothing, quality improvement and Laplacian and Taubin smoothing. The book also discusses the optimization of cable nets and steel frames using nature-inspired metaheuristic methods.

Machine Learning (ML) techniques can automatically generate a model using data from past experiences. ML algorithms can map a reduced data set from real-time measurements of a structure into a detailed/high-fidelity FEA models of the same system. Brevis et al. introduced the concept of Machine Learning minimal-residual (ML-MRes) finite element discretization of PDEs. These methods are tuned within a ML framework and are tailored for the accurate computation of output quantities of interest, regardless of the underlying mesh size. Elementary one dimensional (1D) and 2D elliptic and hyperbolic problems were studied using weight functions up to 20 parameters with artificial neural network (ANN).






ОТСУТСТВУЕТ ССЫЛКА/ НЕ РАБОЧАЯ ССЫЛКА ЕСТЬ РЕШЕНИЕ, ПИШЕМ СЮДА!