scholarly journals Simulation-based machine learning for optoelectronic device design: perspectives, problems, and prospects

2021 ◽  
Vol 53 (4) ◽  
Author(s):  
Joachim Piprek
Keyword(s):  
Machine Learning ◽  
Optoelectronic Device ◽  
Device Design ◽  
Simulation Based

scholarly journals Pitfalls of simulation-based machine learning in optoelectronic device design

2020 ◽  
Author(s):  
Joachim Piprek
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Light Emitting Diode ◽  
Optoelectronic Device ◽  
Point Of View ◽  
Device Physics ◽  
Learning Approaches ◽  
Device Design ◽  
Light Emitting ◽  
Simulation Based

Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates simulation-based machine learning approaches from a device physics point of view. Strategies are suggested for achieving more realistic results.

scholarly journals Pitfalls of simulation-based machine learning in optoelectronic device design

2020 ◽  
Author(s):  
Joachim Piprek
Keyword(s):  
Machine Learning ◽  
Design Optimization ◽  
Light Emitting Diode ◽  
Optoelectronic Device ◽  
Point Of View ◽  
Device Physics ◽  
Learning Approaches ◽  
Device Design ◽  
Light Emitting ◽  
Simulation Based

Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates simulation-based machine learning approaches from a device physics point of view. Strategies are suggested for achieving more realistic results.

scholarly journals Simulation-based Machine Learning for Optoelectronic Device Design: Perspectives, Problems, and Prospects

2021 ◽  
Author(s):  
Joachim Piprek
Keyword(s):  
Machine Learning ◽  
Numerical Simulation ◽  
Real World ◽  
Computational Analysis ◽  
Light Emitting Diode ◽  
Optoelectronic Device ◽  
Point Of View ◽  
Light Emitting ◽  
Simulation Based ◽  
Link Theory

Abstract Numerical simulation and machine learning represent opposite approaches to computational analysis of the real world, deductive vs. inductive. However, both methods suffer from various uncertainties and even their combination often fails to link theory and reality. Focusing on GaN-based light-emitting diode (LED) design optimization, this paper evaluates examples of simulation-based machine learning from a physics point of view. Strategies are suggested for achieving more realistic predictions.

Discrete-Event-Simulation Based on Machine Learning Predictive Agents

2021 ◽  
pp. 609-619
Author(s):  
Javier Andión ◽  
Juan C. Dueñas ◽  
Félix Cuadrado
Keyword(s):  
Machine Learning ◽  
Discrete Event Simulation ◽  
Discrete Event ◽  
Event Simulation ◽  
Simulation Based

DFT band alignment of polar and nonpolar GaN/MgGeN2, ZnO/MgGeN2 and GaN/ZnO heterostructures for optoelectronic device design

2020 ◽  
Vol 533 ◽  
pp. 147503
Author(s):  
Chaiyawat Kaewmeechai ◽  
Yongyut Laosiritaworn ◽  
Atchara Punya Jaroenjittichai
Keyword(s):  
Optoelectronic Device ◽  
Band Alignment ◽  
Device Design ◽  
Nonpolar Gan

scholarly journals DFT analysis of crystal polarity on graphene surface

2021 ◽  
Vol 2015 (1) ◽  
pp. 012105
Author(s):  
Alexander Pavlov ◽  
Alexey Mozharov ◽  
Yury Berdnikov ◽  
Camille Barbier ◽  
Jean-Christophe Harmand ◽  
...  
Keyword(s):  
Density Functional Theory ◽  
Ab Initio ◽  
Density Functional ◽  
Optoelectronic Device ◽  
Device Design ◽  
Theory Analysis ◽  
Functional Theory ◽  
Graphene Surface ◽  
Gan Nanowires ◽  
The Density Functional Theory

Abstract We report an ab-initio study of the preferred polarity for wurtzite GaN nanostructures on virtual graphene substrates. By means of the density functional theory analysis we show that N-polar nanostructures on graphene are energetically favorable in comparison to Ga-polar. These finding are in agreement with experimentally observed N-polarity of wurtzite GaN nanowires grown on graphene substrate. We believe that the revealed polarity preference is of importance for piezoelectric and optoelectronic device design.

scholarly journals Groundwater salt content change and its simulation based on machine learning model in hinterlands of Taklimakan Desert

2013 ◽  
Vol 33 (18) ◽  
pp. 5874-5882
Author(s):  
范敬龙 FAN Jinglong ◽  
刘海龙 LIU Hailong ◽  
雷加强 LEI Jiaqiang ◽  
徐新文 XU Xinwen ◽  
王桂芬 WANG Guifen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Salt Content ◽  
Learning Model ◽  
Taklimakan Desert ◽  
Content Change ◽  
Machine Learning Model ◽  
Simulation Based
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