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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
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DTSTART:20250101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20260821T100000
DTEND;TZID=Asia/Seoul:20260821T120000
DTSTAMP:20260730T035554Z
CREATED:20260730T020529Z
LAST-MODIFIED:20260730T035554Z
UID:12772-1787306400-1787313600@www.ibs.re.kr
SUMMARY:Tangent-Space Regularization for Neural-Network Models of Dynamical Systems - Olive Cawiding
DESCRIPTION:In this talk\, we discuss the paper “Tangent-Space Regularization for Neural-Network Models of Dynamical Systems” by Fredrik B Carlson et al\, arXiv (2026). \nAbstract: \nThis work introduces the concept of tangent space regularization for neural-network models of dynamical systems. The tangent space to the dynamics function of many physical systems of interest in control applications exhibits useful properties\, e.g.\, smoothness\, motivating regularization of the model Jacobian along system trajectories using assumptions on the tangent space of the dynamics. Without assumptions\, large amounts of training data are required for a neural network to learn the full non-linear dynamics without overfitting. We compare different network architectures on one-step prediction and simulation performance and investigate the propensity of different architectures to learn models with correct input-output Jacobian. Furthermore\, the influence of L2 weight regularization on the learned Jacobian eigenvalue spectrum\, and hence system stability\, is investigated.
URL:https://www.ibs.re.kr/bimag/event/tangent-space-regularization-for-neural-network-models-of-dynamical-systems-olive-cawiding/
LOCATION:108\, Conference Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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