作者
Agnese Marcato, Daniel O'Malley, Hari Viswanathan, Eric Guiltinan, Javier E Santos
发表日期
2023/12/14
期刊
arXiv preprint arXiv:2312.09176
简介
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.
学术搜索中的文章
A Marcato, D O'Malley, H Viswanathan, E Guiltinan… - arXiv preprint arXiv:2312.09176, 2023