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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
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DTSTART:20230101T000000
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DTSTART;TZID=Asia/Seoul:20240112T140000
DTEND;TZID=Asia/Seoul:20240112T160000
DTSTAMP:20260425T042400
CREATED:20231229T025818Z
LAST-MODIFIED:20240106T124522Z
UID:8988-1705068000-1705075200@www.ibs.re.kr
SUMMARY:Seokjoo Chae\, AI Feynman: A physics-inspired method for symbolic regression
DESCRIPTION:We will discuss about “AI Feynman: A physics-inspired method for symbolic regression”\,Science Advances 6.16 (2020): eaay2631. \nAbstract \nA core challenge for both physics and artificial intelligence (AI) is symbolic regression: finding a symbolic expression that matches data from an unknown function. Although this problem is likely to be NP-hard in principle\, functions of practical interest often exhibit symmetries\, separability\, compositionality\, and other simplifying properties. In this spirit\, we develop a recursive multidimensional symbolic regression algorithm that combines neural network fitting with a suite of physics-inspired techniques. We apply it to 100 equations from the Feynman Lectures on Physics\, and it discovers all of them\, while previous publicly available software cracks only 71; for a more difficult physics-based test set\, we improve the state-of-the-art success rate from 15 to 90%.
URL:https://www.ibs.re.kr/bimag/event/2024-01-12-jc/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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