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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:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230407T110000
DTEND;TZID=Asia/Seoul:20230407T120000
DTSTAMP:20260529T064729
CREATED:20230213T110215Z
LAST-MODIFIED:20230308T100617Z
UID:7328-1680865200-1680868800@www.ibs.re.kr
SUMMARY:George Karniadakis\, BINNS: Biophysics-Informed Neural Networks
DESCRIPTION:Abstract: We will present a new approach to develop a data-driven\, learning-based framework for predicting outcomes of biophysical systems and for discovering hidden mechanisms and pathways from noisy data. We will introduce a deep learning approach based on neural networks (NNs) and on generative adversarial networks (GANs). Unlike other approaches that rely on big data\, here we “learn” from small data by exploiting the information provided by the mathematical physics\, e.g..\, conservation laws\, reaction kinetics\, etc\,. which are used to obtain informative priors or regularize the neural networks. We will demonstrate how we can train BINNs from multifidelity/multimodality data\, and we will present several examples of inverse problems\, e.g.\, in systems biology for diabetes and in biomechanics for non-invasive inference of thrombus material properties. We will also discuss how operator regression in the form of DeepOnet can be used to accelerate inference based on historical data and only a few new data\, as well its generalization and transfer learning capacity.
URL:https://www.ibs.re.kr/bimag/event/binns-biophysics-informed-neural-networks/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/02/GeorgeKarniadakis.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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