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.
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Posted 04 Mar, 2021
On 24 Mar, 2021
On 28 Feb, 2021
On 10 Jan, 2021
On 05 Jan, 2021
Posted 04 Mar, 2021
On 24 Mar, 2021
On 28 Feb, 2021
On 10 Jan, 2021
On 05 Jan, 2021
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.
Figure 1
Figure 2
Figure 3
Figure 4
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