Holdings: Additive and Advanced Manufacturing, Inverse Problem Methodologies and Machine Learning and Data Science, Volume 4

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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Other Authors:	Kramer, Sharlotte L.B (Editor), Retzlaff, Emily (Editor), Thakre, Piyush (Editor), Hoefnagels, Johan (Editor), Rossi, Marco (Editor), Lattanzi, Attilio (Editor), Hemez, François (Editor), Mirshekari, Mostafa (Editor), Downey, Austin (Editor)
Format:	Electronic eBook
Language:	English
Published:	Cham Springer Nature Switzerland 2024 Cham Springer
Edition:	1st ed. 2024
Series:	Conference Proceedings of the Society for Experimental Mechanics Series
Subjects:	Industrial and Production Engineering Machine Learning Data Science Materials Characterization Technique Industrial engineering Production engineering Machine learning Artificial intelligence / Data processing Materials / Analysis
Online Access:	BHS01 BTU01 FAB01 FAW01 FHA01 FHD01 FHI01 FHM01 FHN01 FHR01 FKE01 FLA01 FRO01 FWS01 FWS02 HTW01 TUM01 UBY01 UER01 Volltext
Physical Description:	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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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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