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Supplementary material: Machine learning based classification of vector field configurations

Supplementary material: Machine learning based classification of vector field configurations
Supplementary material: Machine learning based classification of vector field configurations
The data set contains the supplementary material to support the paper: Machine learning based classification of vector field configurations. The dataset contains simulation files, scripts for data generation and a notebook which shows the steps undertaken to perform the study. The study shows how to cluster magnetisation vector fields into meaningful classes, based on their magnetisation configuration, using an unsupervised machine learning approach.
University of Southampton
Lang, Martin
4b5ae654-6a58-4c2c-a116-87161fcd533d
Pathak, Swapneel Amit
210044c9-174a-4ff9-885b-0fc2e75bfdcb
Holt, Sam
4a88ca9f-0531-40f6-abbf-f52ccd2c2557
Fangohr, Hans
9b7cfab9-d5dc-45dc-947c-2eba5c81a160
Lang, Martin
4b5ae654-6a58-4c2c-a116-87161fcd533d
Pathak, Swapneel Amit
210044c9-174a-4ff9-885b-0fc2e75bfdcb
Holt, Sam
4a88ca9f-0531-40f6-abbf-f52ccd2c2557
Fangohr, Hans
9b7cfab9-d5dc-45dc-947c-2eba5c81a160

(2023) Supplementary material: Machine learning based classification of vector field configurations. University of Southampton doi:10.17617/3.kg33a1 [Dataset]

Record type: Dataset

Abstract

The data set contains the supplementary material to support the paper: Machine learning based classification of vector field configurations. The dataset contains simulation files, scripts for data generation and a notebook which shows the steps undertaken to perform the study. The study shows how to cluster magnetisation vector fields into meaningful classes, based on their magnetisation configuration, using an unsupervised machine learning approach.

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More information

Published date: 1 January 2023

Identifiers

Local EPrints ID: 485195
URI: http://eprints.soton.ac.uk/id/eprint/485195
DOI: doi:10.17617/3.kg33a1
PURE UUID: 699aa764-cf4e-4c4a-b973-0a23f7ad2a14
ORCID for Martin Lang: ORCID iD orcid.org/0000-0001-7104-7867
ORCID for Hans Fangohr: ORCID iD orcid.org/0000-0001-5494-7193

Catalogue record

Date deposited: 30 Nov 2023 18:01
Last modified: 01 Dec 2023 03:03

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Contributors

Contributor: Martin Lang ORCID iD
Contributor: Swapneel Amit Pathak
Contributor: Sam Holt
Contributor: Hans Fangohr ORCID iD

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