Reconstruction of Fields from Sparse Sensing: Differentiable Sensor Placement Enhances Generalization
This addresses the challenge of efficient data collection in scientific and industrial domains where full sensor coverage is infeasible, offering a method to enhance field reconstruction with potential cost and redundancy reductions.
The paper tackled the problem of reconstructing high-dimensional fields from sparse sensor data by introducing a differentiable programming approach to optimize sensor placement during neural network training, resulting in improved test scores on two datasets.
Recreating complex, high-dimensional global fields from limited data points is a grand challenge across various scientific and industrial domains. Given the prohibitive costs of specialized sensors and the frequent inaccessibility of certain regions of the domain, achieving full field coverage is typically not feasible. Therefore, the development of algorithms that intelligently improve sensor placement is of significant value. In this study, we introduce a general approach that employs differentiable programming to exploit sensor placement within the training of a neural network model in order to improve field reconstruction. We evaluated our method using two distinct datasets; the results show that our approach improved test scores. Ultimately, our method of differentiable placement strategies has the potential to significantly increase data collection efficiency, enable more thorough area coverage, and reduce redundancy in sensor deployment.