Search results
- 1.0547633 - ÚI 2022 RIV CH eng J - Journal Article
Dropka, N. - Böttcher, K. - Holeňa, Martin
Development and Optimization of VGF-GaAs Crystal Growth Process Using Data Mining and Machine Learning Techniques.
Crystals. Roč. 11, č. 10 (2021), č. článku 1218. ISSN 2073-4352. E-ISSN 2073-4352
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : VGF-GaAs growth * machine learning * data mining * decision trees * correlation analysis * PCA biplot * k-means clustering
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.670, year: 2021
Method of publishing: Open access
http://dx.doi.org/10.3390/cryst11101218
Permanent Link: http://hdl.handle.net/11104/0323829File Download Size Commentary Version Access 0547633-afin.pdf 3 3.2 MB OA CC BY 4.0 Publisher’s postprint open-access - 2.0541776 - ÚI 2022 RIV CH eng J - Journal Article
Dropka, N. - Ecklebe, S. - Holeňa, Martin
Real Time Predictions of VGF-GaAs Growth Dynamics by LSTM Neural Networks.
Crystals. Roč. 11, č. 2 (2021), č. článku 138. ISSN 2073-4352. E-ISSN 2073-4352
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : neural networks * crystal growth * GaAs * process control * digital twins
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 2.670, year: 2021
Method of publishing: Open access
Permanent Link: http://hdl.handle.net/11104/0319303File Download Size Commentary Version Access 541776-aoa.pdf 2 3.5 MB OA CC BY 4.0 Publisher’s postprint open-access - 3.0505764 - ÚI 2020 RIV NL eng J - Journal Article
Dropka, N. - Holeňa, Martin - Ecklebe, S. - Frank-Rotsch, C. - Winkler, J.
Fast Forecasting of VGF Crystal Growth Process by Dynamic Neural Networks.
Journal of Crystal Growth. Roč. 521, 1 September (2019), s. 9-14. ISSN 0022-0248. E-ISSN 1873-5002
R&D Projects: GA ČR(CZ) GA18-18080S
Institutional support: RVO:67985807
Keywords : Computer simulation * Fluid flows * Gradient freeze technique
OECD category: Condensed matter physics (including formerly solid state physics, supercond.)
Impact factor: 1.632, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1016/j.jcrysgro.2019.05.022
Permanent Link: http://hdl.handle.net/11104/0297153 - 4.0498868 - ÚI 2020 RIV US eng J - Journal Article
Bajer, L. - Pitra, Z. - Repický, J. - Holeňa, Martin
Gaussian Process Surrogate Models for the CMA Evolution Strategy.
Evolutionary Computation. Roč. 27, č. 4 (2019), s. 665-697. ISSN 1063-6560. E-ISSN 1530-9304
R&D Projects: GA ČR GA17-01251S; GA ČR(CZ) GA18-18080S
Grant - others:GA MŠk(CZ) LM2015042
Institutional support: RVO:67985807
Keywords : Black-box optimization * CMA-ES * Gaussian processes * evolution strategies * surrogate modeling
OECD category: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Impact factor: 3.933, year: 2019
Method of publishing: Limited access
http://dx.doi.org/10.1162/evco_a_00244
Permanent Link: http://hdl.handle.net/11104/0291157File Download Size Commentary Version Access 0498868-afin.pdf 13 29.7 MB Publisher’s postprint require 0498868-acc.pdf 8 3.8 MB Proofreading v. Author’s postprint require 0498868subm.pdf 13 2.7 MB Submitted Author´s preprint open-access