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Automated Rediscovery of the Maxwell Equations

Stalzer, Mark and Ju, Chao (2019) Automated Rediscovery of the Maxwell Equations. Applied Sciences, 9 (14). Art. No. 2899. ISSN 2076-3417. doi:10.3390/app9142899.

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There is sufficient information in the far-field of a radiating dipole antenna to rediscover the Maxwell Equations and the wave equations of light, including the speed of light c. We created TheoSea, a Julia program that does this in a few seconds, and the key insight is that the compactness of theories drives the search. The program is a computational embodiment of the scientific method: observation, consideration of candidate theories, and validation.

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Ju, Chao0000-0001-7291-1557
Additional Information:© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( Received: 12 April 2019; Accepted: 20 May 2019; Published: 19 July 2019. Author Contributions: All authors contributed equally to this work. This research was funded by the Gordon and Betty Moore Foundation through Grant GBMF4915 to the Caltech Center for Data-Driven Discovery. Discussions with William Xu of Caltech Math/Computer Science were very helpful. The authors are grateful to S.G. Djorgovski of Caltech Astronomy and V. Chandler of KGI Natural Sciences for their support. The authors declare no conflicts of interest.
Funding AgencyGrant Number
Gordon and Betty Moore FoundationGBMF4915
Subject Keywords:automated discovery; computational science; meta-programming; electrodynamics
Issue or Number:14
Record Number:CaltechAUTHORS:20190719-102327032
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Official Citation:Stalzer, M.; Ju, C. Automated Rediscovery of the Maxwell Equations. Appl. Sci. 2019, 9, 2899; doi: 10.3390/app9142899
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:97291
Deposited By: Tony Diaz
Deposited On:19 Jul 2019 18:07
Last Modified:16 Nov 2021 17:30

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