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X-WR-CALNAME:Biomedical Mathematics Group
X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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BEGIN:VTIMEZONE
TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20240101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250703T160000
DTEND;TZID=Asia/Seoul:20250703T170000
DTSTAMP:20260430T073912
CREATED:20250628T074404Z
LAST-MODIFIED:20250630T055202Z
UID:11207-1751558400-1751562000@www.ibs.re.kr
SUMMARY:Jihun Han - Bridging PDEs and machine learning
DESCRIPTION:Abstract: This talk consists of two main parts. In the first part\, I will discuss a numerical method for solving PDEs based on a stochastic representation of the solution. This approach captures the underlying particle dynamics associated with the physical processes described by the PDE. By aggregating information from the particles’ collective exploration\, the method iteratively reinforces the approximation toward the solution. I will cover its analysis regarding the trainability and highlight its effectiveness across a broad class of problems\, including elliptic equations with interfaces\, multiscale structures\, and perforated domains\, as well as hyperbolic-type problems such as the Eikonal and Burgers equations.\nIn the second part\, I will present a method for learning in-between imagery dynamics. This approach integrates PDE models within latent spaces to enhance both learning capability and interpretability. Notably\, this method demonstrates robustness in capturing intricate dynamics\, such as rotation and outflow\, which pose significant challenges for current state-of-the-art optimal transport methods.
URL:https://www.ibs.re.kr/bimag/event/ji-hoon-han-bridging-pdes-and-machine-learning/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20250721T110000
DTEND;TZID=Asia/Seoul:20250721T120000
DTSTAMP:20260430T073912
CREATED:20250617T084231Z
LAST-MODIFIED:20250617T084231Z
UID:11189-1753095600-1753099200@www.ibs.re.kr
SUMMARY:Jae-Kwang Kim - Weight calibration for causal inference and transfer learning
DESCRIPTION:Abstract: Weight calibration is a popular technique in handling covariate-shift problem in causal inference. It can be viewed as a dual optimization problem for incorporating the implicit regression model. We introduce the generalized entropy calibration as a general tool for weight calibration. Several interesting applications will be introduced in the context of causal inference. Furthermore\, weight calibration can be used to transfer learning\, which combines information from two different samples\, one for source data and the other for target data.
URL:https://www.ibs.re.kr/bimag/event/jae-kwang-kim-weight-calibration-for-causal-inference-and-transfer-learning/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR