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Data Science in Engineering, 2026
Data Science in Engineering, 2026
Knygos.lt klubas Knygos.lt nariams
235,70 €
-15%
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277,29 €
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Data Science in Engineering, Volume 9: Proceedings of the 44th IMAC, A Conference and Exposition on Structural Dynamics, 2026, the ninth volume of ten from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on: Novel Data-Driven Analysis Methods AI-Driven Digital Twins for Structural Modeling and Dynamic Charac…

Data Science in Engineering, 2026 (el. knyga) (skaityta knyga) | knygos.lt

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Data Science in Engineering, Volume 9: Proceedings of the 44th IMAC, A Conference and Exposition on Structural Dynamics, 2026, the ninth volume of ten from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:

  • Novel Data-Driven Analysis Methods
  • AI-Driven Digital Twins for Structural Modeling and Dynamic Characterization
  • Transfer Learning and Population-Based Monitoring
  • Data-Driven Techniques for System Prognostics and Health Monitoring
  • Applications of AI in Structural Dynamics and System Identification
  • Uncertainty Quantification in Data-Driven and Hybrid Models
  • Physics Informed Machine Learning for Dynamic Systems
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Data Science in Engineering, Volume 9: Proceedings of the 44th IMAC, A Conference and Exposition on Structural Dynamics, 2026, the ninth volume of ten from the Conference, brings together contributions to this important area of research and engineering. The collection presents early findings and case studies on fundamental and applied aspects of Data Science in Engineering, including papers on:

  • Novel Data-Driven Analysis Methods
  • AI-Driven Digital Twins for Structural Modeling and Dynamic Characterization
  • Transfer Learning and Population-Based Monitoring
  • Data-Driven Techniques for System Prognostics and Health Monitoring
  • Applications of AI in Structural Dynamics and System Identification
  • Uncertainty Quantification in Data-Driven and Hybrid Models
  • Physics Informed Machine Learning for Dynamic Systems

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