隐式神经表示用于多高度气候数据的同步降维与连续重建
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data
AI总结：
本文提出一种深度学习框架，通过降维、跨模态预测和超分辨率三个组件，实现对多高度风数据的连续重建与高效压缩，在超分辨率和压缩效率上优于现有方法。
摘要：
世界正朝着清洁和可再生能源（如风能）的方向发展，以减少导致全球变暖的温室气体排放。为了增强风数据的分析和存储，我们引入了一个深度学习框架，该框架旨在同时实现从离散观测中对多高度风数据进行有效的降维和连续表示。该框架由三个关键组件组成：降维、跨模态预测和超分辨率。我们的目标是：（1）提高不同气候条件下的数据分辨率，以恢复高分辨率细节；（2）降低数据维度，以便更高效地存储大型气候数据集；（3）实现不同高度测得的风数据之间的交叉预测。综合测试证实，我们的方法在超分辨率质量和压缩效率方面均优于现有方法。
英文摘要：
The world is moving towards clean and renewable energy sources, such as wind energy, in an attempt to reduce greenhouse gas emissions that contribute to global warming. To enhance the analysis and storage of wind data, we introduce a deep learning framework designed to simultaneously enable effective dimensionality reduction and continuous representation of multi-altitude wind data from discrete observations. The framework consists of three key components: dimensionality reduction, cross-modal prediction, and super-resolution. We aim to: (1) improve data resolution across diverse climatic conditions to recover high-resolution details; (2) reduce data dimensionality for more efficient storage of large climate datasets; and (3) enable cross-prediction between wind data measured at different heights. Comprehensive testing confirms that our approach surpasses existing methods in both super-resolution quality and compression efficiency.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
