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X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZOFFSETFROM:+0900
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DTSTART:20220101T000000
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DTSTART;TZID=Asia/Seoul:20230327T160000
DTEND;TZID=Asia/Seoul:20230327T170000
DTSTAMP:20260425T203621
CREATED:20230323T064118Z
LAST-MODIFIED:20230323T064136Z
UID:7536-1679932800-1679936400@www.ibs.re.kr
SUMMARY:Sungwoong Cho\, HyperDeepONet: learning operator with complex target function space using the limited resources via hypernetwork
DESCRIPTION:Fast and accurate predictions for complex physical dynamics are a big challenge across various applications. Real-time prediction on resource-constrained hardware is even more crucial in the real-world problems. The deep operator network (DeepONet) has recently been proposed as a framework for learning nonlinear mappings between function spaces. However\, the DeepONet requires many parameters and has a high computational cost when learning operators\, particularly those with complex (discontinuous or non-smooth) target functions. In this study\, we propose HyperDeepONet\, which uses the expressive power of the hypernetwork to enable learning of a complex operator with smaller set of parameters. The DeepONet and its variant models can be thought of as a method of injecting the input function information into the target function. From this perspective\, these models can be viewed as a special case of HyperDeepONet. We analyze the complexity of DeepONet and conclude that HyperDeepONet needs relatively lower complexity to obtain the desired accuracy for operator learning. HyperDeepONet was successfully applied to various operator learning problems using low computational resources compared to other benchmarks.
URL:https://www.ibs.re.kr/bimag/event/2023-03-27-seminar/
LOCATION:B378 Seminar room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Biomedical Mathematics Seminar
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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