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:20250101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260522T100000
DTEND;TZID=Asia/Seoul:20260522T120000
DTSTAMP:20260503T234743
CREATED:20260429T070216Z
LAST-MODIFIED:20260429T070829Z
UID:12396-1779444000-1779451200@www.ibs.re.kr
SUMMARY:Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations - Gyuyoung Hwang
DESCRIPTION:In this talk\, we discuss the paper “Bridging known and unknown dynamics by transformer-based machine-learning inference from sparse observations” by Zheng-Meng Zhai et al.\, Nature Communications\, 2025. \nAbstract: \nIn applications\, an anticipated issue is where the system of interest has never been encountered before and sparse observations can be made only once. Can the dynamics be faithfully reconstructed? We address this challenge by developing a hybrid transformer and reservoir-computing scheme. The transformer is trained without using data from the target system\, but with essentially unlimited synthetic data from known chaotic systems. The trained transformer is then tested with the sparse data from the target system\, and its output is further fed into a reservoir computer for predicting its long-term dynamics or the attractor. The proposed hybrid machine-learning framework is tested using various prototypical nonlinear systems\, demonstrating that the dynamics can be faithfully reconstructed from reasonably sparse data. The framework provides a paradigm of reconstructing complex and nonlinear dynamics in the situation where training data do not exist and the observations are random and sparse.
URL:https://www.ibs.re.kr/bimag/event/bridging-known-and-unknown-dynamics-by-transformer-based-machine-learning-inference-from-sparse-observations-gyuyoung-hwang/
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260522T110000
DTEND;TZID=Asia/Seoul:20260522T120000
DTSTAMP:20260503T234743
CREATED:20260205T075139Z
LAST-MODIFIED:20260311T121645Z
UID:12194-1779447600-1779451200@www.ibs.re.kr
SUMMARY:Mathematics of diffusive signaling - Alan Lindsay
DESCRIPTION:Diffusive transport is one of the most fundamental mechanisms by which information\, mass\, and chemical signals propagate in physical and biological systems. In many settings—ranging from cellular signaling to chemical sensing—communication is mediated by particles undergoing random motion and interacting with small\, spatially localized targets. This talk explores the mathematical structures underlying diffusive signaling\, emphasizing how geometry\, stochasticity\, and multiscale effects shape signal detection and reliability. Using tools from stochastic processes\, partial differential equations\, and asymptotic analysis\, I will describe how seemingly microscopic features can exert a dominant influence on macroscopic signaling outcomes\, and highlight recent progress on quantifying signal strength\, timing\, and variability in complex geometries. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/mathematics-of-diffusive-signaling-alan-lindsay/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2026/02/alan_lindsay-e1770278281837.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR