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Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept Light & Engineering Vol. 29, No. 1

Light & Engineering 29 (1)

Volume 29
Date of publication 02/27/2021
Pages 56–62

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Discrete Ordinate Radiative Transfer Model With the Neural Network Based Eigenvalue Solver: proof Of Concept Light & Engineering Vol. 29, No. 1
Articles authors:
Dmitry S. Efremenko

Dmitry S. Efremenko received his Ph. D. degree from the Moscow State University in 2011 and the Habilitation degree from MPEI in 2017. Since 2011 he works as a research scientist at the German Aerospace Centre (DLR). He received the Elsevier/JQSRT Goody Award in 2017. In 2020, he obtained a title of Privatdozent from the Technical University of Munich. His scientific interests include radiative transfer, numerical methods and machine learning. He has over 70 scientific papers

Artificial neural networks are attracting increasing attention in various applications. They can be used as ‘universal approximations’, which substitute computationally expensive algorithms by relatively simple sequences of functions, which simulate a reaction of a set of neurons to the incoming signal. In particular, neural networks have proved to be efficient for parameterization of the computationally expensive radiative transfer models (RTMs) in atmospheric remote sensing. Although a direct substitution of RTMs by neural networks can lead to the multiple performance enhancements, such an approach has certain drawbacks, such as loss of generality, robustness issues, etc. In this regard, the neural network is usually trained for a specific application, predefined atmospheric scenarios and a given spectrometer. In this paper a new concept of neural-network based RTMs is examined, in which the neural network substitutes not the whole RTM but rather a part of it (the eigenvalue solver), thereby reducing the computational time while maintaining its generality. The explicit dependencies on geometry of observation and optical thickness of the medium are excluded from training. It is shown that although the speedup factor due to this approach is modest (around 3 times against 103 speed up factor of other approaches reported in recent papers), the resulting neural network is flexible and easy to train. It can be used for arbitrary number of atmospheric layers. Moreover, this approach can be used in conjunction with any RTMs based on the discrete ordinate method. The neural network is applied for simulations of the radiances at the top of the atmosphere in the Huggins band.
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