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Neumann domains on manifolds and graphs Band, Ram
Description
The nodal set of a Laplacian eigenfunction forms a partition of the underlying manifold or graph. Another natural partition is based on the gradient vector field of the eigenfunction (on a manifold) or on the extremal points of the eigenfunction (on a graph). The submanifolds (or subgraphs) of this partition are called Neumann domains. We present the main results concerning Neumann domains on manifolds and on graphs. We compare manifolds to graphs and relate the Neumann domain results on each of them to the nodal domain study. The talk is based on joint works with Lior Alon, Michael Bersudsky, Sebastian Egger, David Fajman and Alexander Taylor.
Item Metadata
Title |
Neumann domains on manifolds and graphs
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Creator | |
Publisher |
Banff International Research Station for Mathematical Innovation and Discovery
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Date Issued |
2018-07-17T15:42
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Description |
The nodal set of a Laplacian eigenfunction forms a partition of the underlying manifold or graph.
Another natural partition is based on the gradient vector field of the eigenfunction (on a manifold) or on the extremal points of the eigenfunction (on a graph).
The submanifolds (or subgraphs) of this partition are called Neumann domains.
We present the main results concerning Neumann domains on manifolds and on graphs.
We compare manifolds to graphs and relate the Neumann domain results on each of them to the nodal domain study.
The talk is based on joint works with Lior Alon, Michael Bersudsky, Sebastian Egger, David Fajman and Alexander Taylor.
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Extent |
56.0
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Subject | |
Type | |
File Format |
video/mp4
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Language |
eng
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Notes |
Author affiliation: Technion
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Series | |
Date Available |
2019-03-13
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivatives 4.0 International
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DOI |
10.14288/1.0376840
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URI | |
Affiliation | |
Peer Review Status |
Unreviewed
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Scholarly Level |
Researcher
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Rights URI | |
Aggregated Source Repository |
DSpace
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Item Media
Item Citations and Data
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International