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SUMMARY:Symbolic regression for discovering physics equations
DTSTART:20250912T090000Z
DTEND:20250912T100000Z
DTSTAMP:20261013T044300Z
UID:indico-event-18033@indico.psi.ch
CONTACT:markus.mueller@psi.ch
DESCRIPTION:Speakers: Arjun Dey (PSI)\n\nIn 1601\, Kepler obtained the bes
 t planetary data and\, after 4 years and 40 failed attempts\, showed that 
 Mars follows an elliptical orbit. This result is an early example of symbo
 lic regression\, and it revealed a simple law behind raw measurements. In 
 this article\, the authors present modern approaches to the same challenge
 \, combining physics methods (dimensional analysis\, smoothness\, symmetry
 \, separability\, compositionality) with modern machine learning to discov
 er equations from data. As a concrete test\, the authors re-discover 100 e
 quations from the Feynman Lectures on Physics.\nRef:\nUdrescu\, S. M.\, T
 egmark\, M. (2020). AI Feynman: A physics-inspired method for symbolic reg
 ression. Science advances\, 6(16)\, eaay2631. https://doi.org/10.1126/sci
 adv.aay2631\n\nhttps://indico.psi.ch/event/18033/
LOCATION:WHGA/121
URL:https://indico.psi.ch/event/18033/
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