Deep neural networks (DNNs) are empirically derived systems that have transformed
traditional research methods, and are driving scientific discovery. Artificial electromagnetic …

" The art of structure is where to put the holes" Robert Le Ricolais, 1894-1977 This is a
completely revised, updated and expanded version of the book titled" Optimization of …

This paper investigates the eigenvalue buckling of functionally graded graphene platelets
(GPLs) reinforced cylindrical shells consisting of multiple layers through finite element …

In this work we investigate the use of deep inverse models (DIMs) for designing artificial
electromagnetic materials (AEMs)–such as metamaterials, photonic crystals, and …

We consider the task of solving generic inverse problems, where one wishes to determine
the hidden parameters of a natural system that will give rise to a particular set of …

We propose a feasible direction approach for the minimization by interior-point algorithms of
a smooth function under smooth equality and inequality constraints. It consists of the iterative …

Purpose The purpose of this article is to analyze the optimization process in depth,
elaborating on the components of the entire process and the techniques used. Researchers …

Abstract Rapid Manufacturing Rapid Prototyping Solid Freeform fabrication Additive
Manufacturing Additive Fabrication Automated Freeform Fabrication Construction 3D …

An algorithm has been developed which uses material as a discrete variable in multi-
material topology optimization and thus provides an alternative to traditional methods using …

In the present paper, a finite element method is used to study the vibrations and buckling of
a piezoelectric nanobeam. The beam theory used here is Bernoulli–Euler model. In order to …