Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review

Y Sun, K Deng, K Ren, J Liu, C Deng, Y Jin - ISPRS Journal of …, 2024 - Elsevier
Nowadays, meteorological data plays a crucial role in various fields such as remote sensing,
weather forecasting, climate change, and agriculture. The regional and local studies call for …

Improving precipitation nowcasting for high-intensity events using deep generative models with balanced loss and temperature data: A case study in the Netherlands

C Cambier van Nooten, K Schreurs… - … Intelligence for the …, 2023 - journals.ametsoc.org
Precipitation nowcasting is essential for weather-dependent decision-making, but it remains
a challenging problem despite active research. The combination of radar data and deep …

OptoGPT: A Versatile Inverse Design Model for Optical Multilayer Thin Film Structures

T Ma, LJ Guo, H Wang - … 2023 Workshop on Deep Learning and …, 2023 - openreview.net
Optical multilayer thin film structures are widely used in various photonic applications.
Inverse design is an important but difficult step to enable these applications, which seeks to …

OL-Transformer: A Fast and Universal Surrogate Simulator for Optical Multilayer Thin Film Structures

T Ma, H Wang, LJ Guo - arXiv preprint arXiv:2305.11984, 2023 - arxiv.org
Deep learning-based methods have recently been established as fast and accurate
surrogate simulators for optical multilayer thin film structures. However, existing methods …

A Systematic Review of Deep Learning Applications in Interpolation and Extrapolation of Precipitation Data

M Sit, BZ Demiray, I Demir - 2022 - eartharxiv.org
With technological enhancements, the volume, velocity, and variety (3Vs) of the raw digital
Earth data have increased in recent years. Due to the increased availability of computer …