Deep Learning Emulator Towards Both Forward and Adjoint Modes of Atmospheric Gas-Phase Chemical Process

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Opis bibliograficzny
Wydane w:	Atmosphere vol. 16, no. 9 (2025), p. 1109-1126
1. autor:	Liu, Yulong
Kolejni autorzy:	Liao Meicheng, Liu, Jiacheng, Cheng, Zhen
Wydane:	MDPI AG
Hasła przedmiotowe:	China Air quality Nitric oxide Datasets Sensitivity analysis Nitrogen dioxide Artificial neural networks Neural networks Chemistry Computer applications Photochemicals Distance learning Simulation Ozone Emulators Numerical models Isoprene Graphics processing units Chemical transport Chemical bonds Hydroxyl radicals Central processing units > CPUs Accuracy Deep learning Atmospheric chemistry Data assimilation Photochemistry Machine learning Graphics Gases Mathematical models Air quality models Ordinary differential equations Embedding
Dostęp online:	Citation/Abstract Full Text + Graphics Full Text - PDF
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