Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation”
Abstract We respond to Schaeben et al.’s1 comment on our paper, “Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.” While their observations are factually correct, they do not disprove our results. Our method, TACS, preserves the full distribution of crystallographic orient...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | npj Computational Materials |
| Online Access: | https://doi.org/10.1038/s41524-025-01562-0 |
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| _version_ | 1850054265371885568 |
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| author | J. Wanni C. A. Bronkhorst D. J. Thoma |
| author_facet | J. Wanni C. A. Bronkhorst D. J. Thoma |
| author_sort | J. Wanni |
| collection | DOAJ |
| description | Abstract We respond to Schaeben et al.’s1 comment on our paper, “Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.” While their observations are factually correct, they do not disprove our results. Our method, TACS, preserves the full distribution of crystallographic orientations and is validated with real-world data. We emphasize the importance of empirical validation over theoretical constructs in assessing machine learning methods’ practical performance. |
| format | Article |
| id | doaj-art-eb561810461c41d889a3a189cdb0d2a3 |
| institution | DOAJ |
| issn | 2057-3960 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Computational Materials |
| spelling | doaj-art-eb561810461c41d889a3a189cdb0d2a32025-08-20T02:52:19ZengNature Portfolionpj Computational Materials2057-39602025-03-011111310.1038/s41524-025-01562-0Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation”J. Wanni0C. A. Bronkhorst1D. J. Thoma2Department of Materials Science and Engineering, University of Wisconsin-MadisonDepartment of Materials Science and Engineering, University of Wisconsin-MadisonDepartment of Materials Science and Engineering, University of Wisconsin-MadisonAbstract We respond to Schaeben et al.’s1 comment on our paper, “Machine Learning Enhanced Analysis of EBSD Data for Texture Representation.” While their observations are factually correct, they do not disprove our results. Our method, TACS, preserves the full distribution of crystallographic orientations and is validated with real-world data. We emphasize the importance of empirical validation over theoretical constructs in assessing machine learning methods’ practical performance.https://doi.org/10.1038/s41524-025-01562-0 |
| spellingShingle | J. Wanni C. A. Bronkhorst D. J. Thoma Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” npj Computational Materials |
| title | Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” |
| title_full | Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” |
| title_fullStr | Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” |
| title_full_unstemmed | Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” |
| title_short | Reply to: Comment on “Machine learning enhanced analysis of EBSD data for texture representation” |
| title_sort | reply to comment on machine learning enhanced analysis of ebsd data for texture representation |
| url | https://doi.org/10.1038/s41524-025-01562-0 |
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