Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4: Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics
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Weitere Verfasser: | , , , , , , , , |
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Format: | Elektronisch E-Book |
Sprache: | English |
Veröffentlicht: |
Cham
Springer Nature Switzerland
2024
Cham Springer |
Ausgabe: | 1st ed. 2024 |
Schriftenreihe: | Conference Proceedings of the Society for Experimental Mechanics Series
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Schlagworte: | |
Online-Zugang: | BHS01 BTU01 FAB01 FAW01 FHA01 FHD01 FHI01 FHM01 FHN01 FHR01 FKE01 FLA01 FRO01 FWS01 FWS02 HTW01 TUM01 UBY01 UER01 Volltext |
Beschreibung: | 1 Online-Ressource (VII, 102 p. 86 illus., 73 illus. in color) |
ISBN: | 9783031504747 |
ISSN: | 2191-5652 |
DOI: | 10.1007/978-3-031-50474-7 |
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author2 | Kramer, Sharlotte L.B Retzlaff, Emily Thakre, Piyush Hoefnagels, Johan Rossi, Marco Lattanzi, Attilio Hemez, François Mirshekari, Mostafa Downey, Austin |
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discipline | Elektrotechnik Werkstoffwissenschaften / Fertigungstechnik Maschinenbau |
discipline_str_mv | Elektrotechnik Werkstoffwissenschaften / Fertigungstechnik Maschinenbau |
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edition | 1st ed. 2024 |
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spellingShingle | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics Industrial and Production Engineering Machine Learning Data Science Materials Characterization Technique Industrial engineering Production engineering Machine learning Artificial intelligence / Data processing Materials / Analysis |
title | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics |
title_auth | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics |
title_exact_search | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics |
title_exact_search_txtP | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics |
title_full | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics edited by Sharlotte L.B. Kramer, Emily Retzlaff, Piyush Thakre, Johan Hoefnagels, Marco Rossi, Attilio Lattanzi, François Hemez, Mostafa Mirshekari, Austin Downey |
title_fullStr | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics edited by Sharlotte L.B. Kramer, Emily Retzlaff, Piyush Thakre, Johan Hoefnagels, Marco Rossi, Attilio Lattanzi, François Hemez, Mostafa Mirshekari, Austin Downey |
title_full_unstemmed | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics edited by Sharlotte L.B. Kramer, Emily Retzlaff, Piyush Thakre, Johan Hoefnagels, Marco Rossi, Attilio Lattanzi, François Hemez, Mostafa Mirshekari, Austin Downey |
title_short | Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4 |
title_sort | additive and advanced manufacturing inverse problem methodologies and machine learning and data science volume 4 proceedings of the 2023 annual conference exposition on experimental and applied mechanics |
title_sub | Proceedings of the 2023 Annual Conference & Exposition on Experimental and Applied Mechanics |
topic | Industrial and Production Engineering Machine Learning Data Science Materials Characterization Technique Industrial engineering Production engineering Machine learning Artificial intelligence / Data processing Materials / Analysis |
topic_facet | Industrial and Production Engineering Machine Learning Data Science Materials Characterization Technique Industrial engineering Production engineering Machine learning Artificial intelligence / Data processing Materials / Analysis |
url | https://doi.org/10.1007/978-3-031-50474-7 |
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