Application of finite element method and artificial neural network to the die design of radial forging processes

2004 ◽  
Vol 24 (9-10) ◽  
pp. 700-707 ◽  
Author(s):  
Su-Hai Hsiang ◽  
Huey-Lin Ho
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Die Design ◽  
Radial Forging ◽  
Artificial Neural ◽  
Element Method

Multi-physics Analysis of Multi-chip LED Light Source Based on Finite Element Method and Artificial Neural Network

2019 ◽  
Vol 40 (6) ◽  
pp. 795-802 ◽  
Author(s):  
刘宏伟 LIU Hong-wei ◽  
牛萍娟 YU Dan-dan ◽  
郭 凯 NIU Ping-juan ◽  
张建新 ZHANG Zan-yun ◽  
王 闯 GUO Kai ◽  
...  
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Light Source ◽  
Led Light ◽  
Artificial Neural ◽  
Element Method

scholarly journals Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

Nanomaterials ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 170
Author(s):  
Sneha Verma ◽  
Sunny Chugh ◽  
Souvik Ghosh ◽  
B. M. Azizur Rahman
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Plasmonic Nanostructures ◽  
Optical Parameters ◽  
Complex Data ◽  
The Neural Network ◽  
Artificial Neural ◽  
Element Method

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

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