Reliably reconstructing physical fields from sparse sensor data is a
challenge that frequently arises in many scientific domains. In practice, the
process generating the data often is not understood to sufficient accuracy.
Therefore, there is a growing interest in using the deep neural network route
to address the problem. This work presents a novel approach that learns a
continuous representation of the physical field using implicit neural
representations (INRs). Specifically, after factorizing spatiotemporal
variability into spatial and temporal components using the separation of
variables technique, the method learns relevant basis functions from sparsely
sampled irregular data points to develop a continuous representation of the
data. In experimental evaluations, the proposed model outperforms recent INR
methods, offering superior reconstruction quality on simulation data from a
state-of-the-art climate model and a second dataset that comprises ultra-high
resolution satellite-based sea surface temperature fields.

本研究采用隐式神经表示方法，从稀疏的传感器数据中可靠地重构物理场。通过将时空变化分解为空间和时间分量，并利用因变量分离技术从稀疏采样的不规则数据点中学习相关基函数，从而发展出数据的连续表示。在实验评估中，该模型在模拟数据和卫星海面温度数据集上表现出优越的重构质量，超过了最近的隐式神经表示方法。