The twenty-five papers in this special cluster issue focus on machine learning applications in electromagnetics. The terms “machine learning” and “artificial intelligence” were coined in the mid-1950s, but their mathematical foundations were rooted many decades earlier. While the term “artificial intelligence” (AI for short) has a broader context encompassing many different domains from neuroscience to algorithm development in computer science, the term “machine learning” (ML for short) focuses on the practical aspect of AI, i.e., applying mathematics to solve unique problems and to teach them to machines. ML methods can focus on creating suitable algorithms to solve novel problems or to automate existing solutions mainly by leveraging vast amounts of data. n this special cluster issue, we are exploring the ingenuity of the researchers for applications of ML methods in electromagnetics, antennas, and propagation

Guest Editorial: Special Cluster on Machine Learning Applications in Electromagnetics, Antennas, and Propagation / Bayraktar, Z.; Anagnostou, D. E.; Goudos, S. K.; Campbell, S. D.; Werner, D. H.; Massa, A.. - In: IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS. - ISSN 1536-1225. - STAMPA. - 18:11(2019), pp. 2220-2224. [10.1109/LAWP.2019.2945426]

Guest Editorial: Special Cluster on Machine Learning Applications in Electromagnetics, Antennas, and Propagation

Werner D. H.;Massa A.
2019

Abstract

The twenty-five papers in this special cluster issue focus on machine learning applications in electromagnetics. The terms “machine learning” and “artificial intelligence” were coined in the mid-1950s, but their mathematical foundations were rooted many decades earlier. While the term “artificial intelligence” (AI for short) has a broader context encompassing many different domains from neuroscience to algorithm development in computer science, the term “machine learning” (ML for short) focuses on the practical aspect of AI, i.e., applying mathematics to solve unique problems and to teach them to machines. ML methods can focus on creating suitable algorithms to solve novel problems or to automate existing solutions mainly by leveraging vast amounts of data. n this special cluster issue, we are exploring the ingenuity of the researchers for applications of ML methods in electromagnetics, antennas, and propagation
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Bayraktar, Z.; Anagnostou, D. E.; Goudos, S. K.; Campbell, S. D.; Werner, D. H.; Massa, A.
Guest Editorial: Special Cluster on Machine Learning Applications in Electromagnetics, Antennas, and Propagation / Bayraktar, Z.; Anagnostou, D. E.; Goudos, S. K.; Campbell, S. D.; Werner, D. H.; Massa, A.. - In: IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS. - ISSN 1536-1225. - STAMPA. - 18:11(2019), pp. 2220-2224. [10.1109/LAWP.2019.2945426]
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11572/249641
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