Deep learning of nonlinear flame fronts development due to Darrieus–Landau instability
The Darrieus–Landau instability is studied using a data-driven, deep neural network approach. The task is set up to learn a time-advancement operator mapping any given flame front to a future time. A recurrent application of such an operator rolls out a long sequence of predicted flame fronts, and a...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2023-06-01
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| Series: | APL Machine Learning |
| Online Access: | http://dx.doi.org/10.1063/5.0139857 |