Accurately reconstructing a global spatial field from sparse data has been a longstanding problem in several domains, such as Earth Sciences and Fluid Dynamics. Historically, scientists have approached this problem by employing complex physics models to reconstruct the spatial fields. However, these methods are often computationally intensive. With the increase in popularity of machine learning (ML), several researchers have applied ML to the spatial field reconstruction task and observed improvements in computational efficiency. One such method in arXiv:2101.00554 utilizes a sparse mask of sensor locations and a Voronoi tessellation with sensor measurements as inputs to a convolutional neural network for reconstructing the global spatial field. In this work, we propose multiple adjustments to the aforementioned approach and show improvements on geoscience and fluid dynamics simulation datasets. We identify and discuss scenarios that benefit the most using the proposed ML-based spatial field reconstruction approach.

翻译：从稀疏数据中精确重建全局空间场一直是地球科学和流体动力学等多个领域长期存在的问题。历史上，科学家通过采用复杂的物理模型来解决空间场重建问题，但这些方法通常计算成本高昂。随着机器学习（ML）的普及，多位研究者将ML应用于空间场重建任务，并观察到计算效率的提升。例如arXiv:2101.00554中提出的方法，利用传感器位置的稀疏掩码及以传感器测量值为输入的Voronoi图，通过卷积神经网络重建全局空间场。本研究针对该方法提出多项改进方案，并在地球科学和流体动力学仿真数据集上验证了性能提升。我们进一步识别并讨论了所提出的基于ML的空间场重建方法最具优势的应用场景。