HIGHLIGHTS
- who: Rixin Yu and colleagues from the Department of Energy Sciences, Lund University, Lund, Sweden have published the research work: Deep learning of nonlinear flame fronts development due to Darrieus-Landau instability, in the Journal: (JOURNAL)
- what: Using two datasets of flame front solutions obtained from a heavyduty direct numerical simulation and a light-duty modeling equation the authors compare the performance of three state-of-art operator-regression network methods: convolutional neural networks Fourier neural operator (FNO) and deep operator network. The authors show that for learning complicated front evolution FNO gives the best . . .
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