Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F21%3A00541776" target="_blank" >RIV/67985807:_____/21:00541776 - isvavai.cz</a>
Result on the web
<a href="http://hdl.handle.net/11104/0319303" target="_blank" >http://hdl.handle.net/11104/0319303</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/cryst11020138" target="_blank" >10.3390/cryst11020138</a>
Alternative languages
Result language
angličtina
Original language name
Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
Original language description
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. Real time predictions of the temperatures and solid–liquid interface position in GaAs are crucial for control applications and for process visualization, i.e., for generation of digital twins. In the reported study, an LSTM network was trained on 1950 datasets with 2 external inputs and 6 outputs. Based on network performance criteria and training results, LSTMs showed the very accurate predictions of the VGF-GaAs growth process with median root-mean-square-error (RMSE) values of 2 × 10−3. This deep learning method achieved a superior predictive accuracy and timeliness compared with more traditional Nonlinear AutoRegressive eXogenous (NARX) recurrent networks.
Czech name
—
Czech description
—
Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/GA18-18080S" target="_blank" >GA18-18080S: Fusion-Based Knowledge Discovery in Human Activity Data</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Crystals
ISSN
2073-4352
e-ISSN
2073-4352
Volume of the periodical
11
Issue of the periodical within the volume
2
Country of publishing house
CH - SWITZERLAND
Number of pages
13
Pages from-to
138
UT code for WoS article
000622430500001
EID of the result in the Scopus database
2-s2.0-85103909154