Cite
How machine learning approaches are useful in computing the optical properties of non-spherical particles across a broad range of size parameters?
MLA
Bi, Lei, et al. “How Machine Learning Approaches Are Useful in Computing the Optical Properties of Non-Spherical Particles across a Broad Range of Size Parameters?” Journal of Quantitative Spectroscopy & Radiative Transfer, vol. 323, Sept. 2024, p. N.PAG. EBSCOhost, https://doi.org/10.1016/j.jqsrt.2024.109057.
APA
Bi, L., Xi, Y., Han, W., & Du, Z. (2024). How machine learning approaches are useful in computing the optical properties of non-spherical particles across a broad range of size parameters? Journal of Quantitative Spectroscopy & Radiative Transfer, 323, N.PAG. https://doi.org/10.1016/j.jqsrt.2024.109057
Chicago
Bi, Lei, Yue Xi, Wei Han, and Zhenhong Du. 2024. “How Machine Learning Approaches Are Useful in Computing the Optical Properties of Non-Spherical Particles across a Broad Range of Size Parameters?” Journal of Quantitative Spectroscopy & Radiative Transfer 323 (September): N.PAG. doi:10.1016/j.jqsrt.2024.109057.