@inproceedings{61b3da5beebc4e23be85f7eba886f7bc,
title = "Active learning-based efficient separation risk assessment in national airspace system",
abstract = "The real-time risk assessment is critical for the decision making and the safety of the National Airspace System (NAS). The adaptive kriging model has the benefit that it can achieve accurate results compared with the sample-based method such as Monte Carlo simulation but with less training data. That is used to develop a framework for the efficient risk assessment (both time-independent and time-dependent problems) in NAS. The framework can be used to the problems with different types of uncertainties and multiple well-defined safety metrics. The efficiency and accuracy of the methods are demonstrated by case studies of risk assessment loss of separation and hazardous weather avoidance.",
author = "Yi Gao and Yongming Liu and Parikshit Dutta and Oliver Chen and Iyer, {Hari N.} and Yang, {Bong Jun}",
note = "Funding Information: The work related to this research were performed at the Prognostic Analysis and Reliability Assessment Lab at Arizona State University. The research reported in this paper was supported by funds from NASA University Leadership Initiative program (Contract No. NNX17AJ86A, Project Officer: Dr. Kai Geobel and Dr. Anupa Bajwa, Principal Investigator: Dr. Yongming Liu). The support is gratefully acknowledged. Publisher Copyright: {\textcopyright} 2019, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.; AIAA Aviation 2019 Forum ; Conference date: 17-06-2019 Through 21-06-2019",
year = "2019",
doi = "10.2514/6.2019-2942",
language = "English (US)",
isbn = "9781624105890",
series = "AIAA Aviation 2019 Forum",
publisher = "American Institute of Aeronautics and Astronautics Inc, AIAA",
booktitle = "AIAA Aviation 2019 Forum",
}