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Activity Number: 28 - Computation, Design, and Quality Assurance of Physical Science and Engineering Applications
Type: Contributed
Date/Time: Sunday, August 8, 2021 : 1:30 PM to 3:20 PM
Sponsor: Quality and Productivity Section
Abstract #318194
Title: Neural Network for Prediction of Functional Data
Author(s): Weiru Han* and Lu Lu and Jiangfeng Zhou
Companies: Department of Mathematics & Statistics, University of South Florida and University of South Florida and Department of Physics, University of South Florida
Keywords: multi-layer perceptron; functional principal component analysis; factorial design experiment; evolutionary algorithm; multiple objective optimization

Neural networks are popular for classification and prediction involving more input than output variables. However, they have not been broadly used for predicting functional output. Motivated from a physical science study which involves predicting the spectrum curve from metamaterial optical device with different geometrical parameter settings, we developed neural network models that utilize the multi-layer perceptron for predicting the functional output. To optimize the network architecture, first a screening design was used to explore the impact of model hyperparameters such as the numbers of layers and nodes and the choices of activation functions. Then a two-phased genetic algorithm was developed to search the optimal structures to simultaneously minimize the training and prediction errors as well as the model complexity. The first phase uses a discrete non-dominated sorting genetic algorithm to seek top-ranking combinations of the number of nodes, activation functions and optimization methods. The second phases uses a continuous search focusing on optimizing the number of nodes. The proposed method will be illustrated with the optical device optimization example.

Authors who are presenting talks have a * after their name.

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