Title	Dimension reduction of the input parameter space of vector-valued functions
Creator	Zahm, Olivier
Publisher	Banff International Research Station for Mathematical Innovation and Discovery
Date Issued	2017-10-09T11:44
Description	Approximation of multivariate functions is a difficult task when the number of input parameters is large. Identifying the directions where the function doesn't significantly vary is a key step for complexity reduction. Among other dimension reduction techniques, the Active Subspace method uses gradients of a scalar-valued function to reduce the parameter space. In this talk, we extend this methodology for vector-valued functions, e.g. functions with multiple scalar outputs or functions taking values in function spaces. Numerical examples reveal the importance of the choice of the metric to measure errors.
Extent	20 minutes
Subject	Mathematics; Probability theory and stochastic processes; Numerical analysis; Industrial mathematics / modelling and simulation
Type	Moving Image
File Format	video/mp4
Language	eng
Notes	Author affiliation: MIT
Series	BIRS Workshop Lecture Videos (Banff, Alta)
Date Available	2018-04-08
Provider	Vancouver : University of British Columbia Library
Rights	Attribution-NonCommercial-NoDerivatives 4.0 International
DOI	10.14288/1.0365238
URI	http://hdl.handle.net/2429/65166
Affiliation	Non UBC
Peer Review Status	Unreviewed
Scholarly Level	Postdoctoral
Rights URI	http://creativecommons.org/licenses/by-nc-nd/4.0/
Aggregated Source Repository	DSpace

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