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DTSTART;TZID=America/Los_Angeles:20260427T161500
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DTSTAMP:20260425T000639
CREATED:20260424T155240Z
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UID:4091-1777306500-1777310100@colleges.claremont.edu
SUMMARY:From ICON to GenICON: In-Context Operator Learning with Uncertainty Quantification (Siting Liu\, UCR)
DESCRIPTION:Abstract: I will introduce In-Context Operator Networks (ICON)\, a framework in which a single neural network learns solution operators for differential equations directly from a few prompted input-output examples at inference time\, without any weight updates. ICON acts as a few-shot learner across forward and inverse problems for ODEs\, PDEs\, and mean-field control. I will then present a probabilistic interpretation: under a random differential equation data model\, ICON implicitly computes the posterior predictive mean given the context\, linking operator learning to Bayesian inference. This motivates GenICON\, a generative variant that samples from the posterior predictive for principled uncertainty quantification\, yielding a unified Bayesian view of in-context operator learning.
URL:https://colleges.claremont.edu/ccms/event/from-icon-to-genicon-in-context-operator-learning-with-uncertainty-quantification-siting-liu-ucr/
LOCATION:Emmy Noether Room\, Estella 1021\, Pomona College\,\, 610 N. College Ave.\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Ryan Aschoff":MAILTO:ryan.aschoff@cgu.edu
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