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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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DTSTART:20250101T000000
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DTSTART;TZID=Asia/Seoul:20260731T100000
DTEND;TZID=Asia/Seoul:20260731T120000
DTSTAMP:20260730T020723Z
CREATED:20260629T081120Z
LAST-MODIFIED:20260730T020723Z
UID:12661-1785492000-1785499200@www.ibs.re.kr
SUMMARY:Fast training of accurate physics-informed neural networks without gradient descent - Seunghun Lee
DESCRIPTION:In this talk\, we discuss the paper “Fast training of accurate physics-informed neural networks without gradient descent” by Chinmay Datar et al.\, ICLR\, 2026. \nAbstract: \nSolving time-dependent Partial Differential Equations (PDEs) is one of the most critical problems in computational science. While Physics-Informed Neural Networks (PINNs) offer a promising framework for approximating PDE solutions\, their accuracy and training speed are limited by two core barriers: gradient-descent-based iterative optimization over complex loss landscapes and non-causal treatment of time as an extra spatial dimension. We present Frozen-PINN\, a novel PINN based on the principle of space-time separation that leverages random features instead of training with gradient descent\, and incorporates temporal causality by construction. On nine PDE benchmarks\, including challenges like extreme advection speeds\, shocks\, and high-dimensionality\, Frozen-PINNs achieve superior training efficiency and accuracy over state-of-the-art PINNs\, often by several orders of magnitude. Our work addresses longstanding training and accuracy bottlenecks of PINNs\, delivering quickly trainable\, highly accurate\, and inherently causal PDE solvers\, a combination that prior methods could not realize. Our approach challenges the reliance of PINNs on stochastic gradient-descent-based methods and specialized hardware\, leading to a paradigm shift in PINN training and providing a challenging benchmark for the community.
URL:https://www.ibs.re.kr/bimag/event/oscillatory-network-efficiency-predicts-mood-and-fatigue-during-sleep-deprivation-seunghun-lee/
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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