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Application of Artificial and recurrent neural network on the steady-state and transient finite element modeling
Conference paper

Application of Artificial and recurrent neural network on the steady-state and transient finite element modeling

Cadmus Yuan, Yu-Jun Hong, Chang-Chi Lee, Kou-Ning Chiang and Jin-Huang Huang
2019 20th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and Microsystems, EuroSimE 2019, 8724570
03/2019

Abstract

Electronic Optical and Magnetic Materials Fluid Flow and Transfer Processes Electrical and Electronic Engineering Mechanical Engineering Safety Risk Reliability and Quality Modeling and Simulation
Artificial intelligence techniques have been widely applied in many domains, such as image /sound/text recognition, manufacturing monitoring, etc. One of the requirements for an artificial intelligence modeling is massive datasets. However, it is often limited knowns in the beginning of the design phase.This paper studied the methods and the influence of building an artificial intelligence model from a limited number of inputs. The application of the artificial neural network (ANN) and the recurrent neural network (RNN) has been applied to the nonlinear mechanical FE, steady-state thermal FE and transient FE model, and a rather simple neural network model and accuracy/application of these models has been reported.

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