Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
The aim of this study was to assess the aptitude of the recurrent Long Short-Term Memory (LSTM) neural networks for fast and accurate predictions of process dynamics in vertical-gradient-freeze growth of gallium arsenide crystals (VGF-GaAs) using datasets generated by numerical transient simulations...
Main Authors: | Natasha Dropka, Stefan Ecklebe, Martin Holena |
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Format: | Article |
Language: | English |
Published: |
MDPI AG
2021-01-01
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Series: | Crystals |
Subjects: | |
Online Access: | https://www.mdpi.com/2073-4352/11/2/138 |
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