Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks
- DOI
- 10.3390/cryst11020138
- Published
- 2021-01-29
- Container
- Crystals
- Publisher
- MDPI AG
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3390/cryst11020138,
title = {Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks},
author = {Natasha Dropka and Stefan Ecklebe and Martin Holena},
year = {2021},
journal = {Crystals},
doi = {10.3390/cryst11020138},
url = {https://doi.org/10.3390/cryst11020138}
}RIS
TY - JOUR TI - Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks AU - Natasha Dropka AU - Stefan Ecklebe AU - Martin Holena PY - 2021 JO - Crystals DO - 10.3390/cryst11020138 UR - https://doi.org/10.3390/cryst11020138 ER -
APA
Dropka, N., Ecklebe, S., & Holena, M. (2021). Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks. Crystals. https://doi.org/10.3390/cryst11020138
Source records
- crossref · retrieved 2026-09-25T05:43:51.684Z