BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20210101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20220401T130000
DTEND;TZID=Asia/Seoul:20220401T140000
DTSTAMP:20260424T184737
CREATED:20220331T190000Z
LAST-MODIFIED:20220320T093117Z
UID:5564-1648818000-1648821600@www.ibs.re.kr
SUMMARY:Physics-informed learning of governing equations from scarce data
DESCRIPTION:We will discuss about “Physics-informed learning of governing equations from scarce data”\, Chen et al.\, Nature Communications\, 2021 \nAbstract: Extracting governing equations from data is a central challenge in many diverse areas of science and engineering. Data are abundant whereas models often remain elusive\, as in climate science\, neuroscience\, ecology\, finance\, and epidemiology\, to name only a few examples. In this work\, we combine sparsity-promoting techniques and machine learning with nonlinear dynamical systems to discover governing equations from noisy measurement data. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics\, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. In particular\, we use sparse regression to determine the fewest terms in the dynamic governing equations required to accurately represent the data. This results in parsimonious models that balance accuracy with model complexity to avoid overfitting. We demonstrate the algorithm on a wide range of problems\, from simple canonical systems\, including linear and nonlinear oscillators and the chaotic Lorenz system\, to the fluid vortex shedding behind an obstacle. The fluid example illustrates the ability of this method to discover the underlying dynamics of a system that took experts in the community nearly 30 years to resolve. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing.
URL:https://www.ibs.re.kr/bimag/event/2022-04-01/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR