  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.
