@Article{NMTMA-13-4,
author = {Jingrun, Chen and Lyu, Liyao and Zhiwen, Zhang and Jingrun, Chen},
title = {A QMC-Deep Learning Method for Diffusivity Estimation in Random Domains},
journal = {Numerical Mathematics: Theory, Methods and Applications},
year = {2020},
volume = {13},
number = {4},
pages = {908--927},
abstract = {<p style="text-align: justify;">Exciton diffusion plays a vital role in the function of many organic semiconducting opto-electronic devices, where an accurate description requires precise
control of heterojunctions. This poses a challenging problem because the parameterization of heterojunctions in high-dimensional random space is far beyond the
capability of classical simulation tools. Here, we develop a novel method based on
Quasi-Monte Carlo sampling to generate the training data set and deep neural network to extract a function for exciton diffusion length on surface roughness with
high accuracy and unprecedented efficiency, yielding an abundance of information
over the entire parameter space. Our method provides a new strategy to analyze the
impact of interfacial ordering on exciton diffusion and is expected to assist experimental design with tailored opto-electronic functionalities.</p>},
issn = {2079-7338},
doi = {https://doi.org/10.4208/nmtma.OA-2020-0032},
url = {https://global-sci.com/article/90373/a-qmc-deep-learning-method-for-diffusivity-estimation-in-random-domains}
}