Author:
Yu Jinhe,Bi Lei,Han Wei,Zhang Xiaoye
Abstract
AbstractRadiative transfer simulations and remote sensing studies fundamentally require accurate and efficient computation of the optical properties of non-spherical particles. This paper proposes a deep learning (DL) scheme in conjunction with an optical property database to achieve this goal. Deep neural network (DNN) architectures were obtained from a dataset of the optical properties of super-spheroids with extensive shape parameters, size parameters, and refractive indices. The dataset was computed through the invariant imbedding T-matrix method. Four separate DNN architectures were created to compute the extinction efficiency factor, single-scattering albedo, asymmetry factor, and phase matrix. The criterion for designing these neural networks was the achievement of the highest prediction accuracy with minimal DNN parameters. The numerical results demonstrate that the determination coefficients are greater than 0.999 between the prediction values from the neural networks and the truth values from the database, which indicates that the DNN can reproduce the optical properties in the dataset with high accuracy. In addition, the DNN model can robustly predict the optical properties of particles with high accuracy for shape parameters or refractive indices that are unavailable in the database. Importantly, the ratio of the database size (∼127 GB) to that of the DNN parameters (∼20 MB) is approximately 6810, implying that the DNN model can be treated as a highly compressed database that can be used as an alternative to the original database for real-time computing of the optical properties of non-spherical particles in radiative transfer and atmospheric models.
Publisher
Springer Science and Business Media LLC
Reference79 articles.
1. Abadi, M., and Coauthors, 2016: TensorFlow: A system for large-scale machine learning. Proc. 12th USENIX Symp. on Operating Systems Design and Implementation, Savannah, USENIX, 265–283.
2. Barr, A. H., 1981: Superquadrics and angle-preserving transformations. IEEE Computer Graphics and Applications, 1, 11–23, https://doi.org/10.1109/MCG.1981.1673799.
3. Bengio, Y., 2009: Learning deep architectures for AI. Foundations and Trends® in Machine Learning, 2, 1–127, https://doi.org/10.1561/2200000006.
4. Bi, L., and P. Yang, 2014: Accurate simulation of the optical properties of atmospheric ice crystals with the invariant imbedding T-matrix method. Journal of Quantitative Spectroscopy and Radiative Transfer, 138, 17–35, https://doi.org/10.1016/j.jqsrt.2014.01.013.
5. Bi, L., and P. Yang, 2017: Improved ice particle optical property simulations in the ultraviolet to far-infrared regime. Journal of Quantitative Spectroscopy and Radiative Transfer, 189, 228–237, https://doi.org/10.1016/j.jqsrt.2016.12.007.
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献