BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Biomedical Mathematics Group - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Biomedical Mathematics Group
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:20260410T110000
DTEND;TZID=Asia/Seoul:20260410T120000
DTSTAMP:20260410T014248
CREATED:20260205T074338Z
LAST-MODIFIED:20260311T121617Z
UID:12182-1775818800-1775822400@www.ibs.re.kr
SUMMARY:A Data-Driven Computational Framework for Identifiability and Nonlinear Dynamics Discovery in Complex Systems - Wenrui Hao
DESCRIPTION:Data-driven modeling is essential for deciphering complex biological systems\, yet its utility is often constrained by two fundamental hurdles: the inability to guarantee parameter identifiability and the high computational cost of learning nonlinear dynamics. This talk introduces a unified computational framework designed to overcome these challenges\, bridging theoretical rigor with scalable machine learning. \n\nThe first component of the framework establishes a computational foundation for practical identifiability. By leveraging the Fisher Information Matrix and its theoretical links to coordinate identifiability\, we propose an efficient method for identifiability assessment. We further introduce regularization-based strategies to manage non-identifiable parameters\, thereby enhancing model reliability and facilitating robust uncertainty quantification. \n\nTo address the discovery of nonlinear dynamics\, we present the Laplacian Eigenfunction-Based Neural Operator (LE-NO). This operator learning framework is specifically engineered for modeling reaction–diffusion equations. By projecting nonlinear operators onto Laplacian eigenfunctions\, LE-NO achieves superior computational efficiency and generalization across varying boundary conditions\, effectively bypassing the limitations of large-scale architectures and data scarcity. \n\nFinally\, we demonstrate the framework’s utility in the context of Alzheimer’s disease modeling. We show that this integrated approach ensures reliable parameter inference while capturing the intricate nonlinear dynamics of disease progression\, providing a critical step toward the development of high-fidelity digital twins for neurodegenerative pathology. \n  \nZoom : 997 8258 4700 (pw : 1234)
URL:https://www.ibs.re.kr/bimag/event/a-data-driven-computational-framework-for-identifiability-and-nonlinear-dynamics-discovery-in-complex-systems-wenrui-hao/
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/Wenrui-Hao-2-e1770278378786.jpg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
END:VCALENDAR