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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
BEGIN:STANDARD
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:20260425T221446
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
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20230407T140000
DTEND;TZID=Asia/Seoul:20230407T160000
DTSTAMP:20260425T221446
CREATED:20230331T040259Z
LAST-MODIFIED:20230331T040312Z
UID:7562-1680876000-1680883200@www.ibs.re.kr
SUMMARY:Yun Min Song\, The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms
DESCRIPTION:We will discuss about “The ups and downs of biological oscillators: A comparison of time-delayed negative feedback mechanisms”\,Rombouts\, Jan\, Sarah Verplaetse\, and Lendert Gelens.\, bioRxiv (2023) \nAbstract \n\n\n\nMany biochemical oscillators are driven by the periodic rise and fall of protein concentrations or activities. A negative feedback loop underlies such oscillations. The feedback can act on different parts of the biochemical network. Here\, we mathematically compare time-delay models where the feedback affects production and degradation. We show a mathematical connection between the linear stability of the two models\, and derive how both mechanisms impose different constraints on the production and degradation rates that allow oscillations. We show how oscillations are affected by the inclusion of a distributed delay\, of double regulation (acting on production and degradation)\, and of enzymatic degradation.
URL:https://www.ibs.re.kr/bimag/event/2023-04-07-jc/
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
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