@article{c1fb8b30b0a6489d98f6a05e3b40b482,
title = "Evolving Gaussian Processes and Kernel Observers for Learning and Control in Spatiotemporally Varying Domains: With Applications in Agriculture, Weather Monitoring, and Fluid Dynamics",
abstract = "Monitoring and modeling large-scale stochastic phenomena with both spatial and temporal (spatiotemporal) evolution by using a network of distributed sensors is a critical problem in many control applications (see 'Summary'). Consider, for example, a team of robots that has the task of destroying herbicide-resistant weeds on a farm (see Figure 1 and 'Key Control Problems in Agriculture'). This team must predict weed growth across the whole farm to make intelligent, coordinated decisions [1].",
author = "Whitman, {Joshua E.} and Harshal Maske and Kingravi, {Hassan A.} and Girish Chowdhary",
note = "Funding Information: The work presented in this article was supported in part by the U.S. Air Force Office of Scientific Research under grant FA9550-15-1-0146 and by the United States Department of Agriculture/National Science Foundation under cyber-physical systems grant 2018-67007-28379, and National Robotics Initiative grant 2019-67021-28989. We thank Earth-Sense (www.earthsense.co) for providing approval to use Figures 1 and S2. We also thank Prof. Stephen Long, Prof. Carl Bernacchi, Prof. Michael Gore, and Prof. Edward Publisher Copyright: {\textcopyright} 1991-2012 IEEE.",
year = "2021",
month = feb,
doi = "10.1109/MCS.2020.3032801",
language = "English (US)",
volume = "41",
pages = "30--69",
journal = "IEEE Control Systems",
issn = "1066-033X",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "1",
}