This event is part of Uncertainty Quantification and AI for Complex Systems View Details

Kernel Methods in Uncertainty Quantification and Experimental Design

Description

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This workshop will focus on mathematical foundations and methodological developments in kernel methods for efficiently learning and predicting complex systems. Topics of interest will encompass probabilistic approaches to prediction, integration, optimization, approximate inference, and how these can be leveraged in the design of real and computer experiments.

Organizers

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M A
Mihai Anitescu Argonne National Laboratory
D G
David Ginsbourger University of Bern
F H
Fred Hickernell Illinois Institute of Technology
S L
Shiwei Lan Arizona State University
D S
Daniel Sanz-Alonso University of Chicago
D W
Dave Woods University of Southampton

Registration

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