Data Science in Engineering, Vol. 11 (eBook, PDF)

Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics 2025
Redaktion: Matarazzo, Thomas; Downey, Austin; Tronci, Eleonora Maria; Hemez, François

• Format: PDF

Produktbeschreibung

Data Science in Engineering, Volume 11: Proceedings of the 43rd IMAC, A Conference and Exposition on Structural Dynamics, 2025, the eleventh volume of twelve 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
• Advanced Techniques for Real-Time Monitoring and Predictive Analysis

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 54
• Erscheinungstermin: 22. Januar 2026
• Englisch
• ISBN-13: 9788743807438
• Artikelnr.: 76085201

Autorenporträt

Thomas Matarazzo, François Hemez, Eleonora Maria Tronci, Austin Downey

Inhaltsangabe

1. Data-Driven Method for Reduced Order Modeling of Blisks with Large and
Small Mistuning 2. System Identification of Data from Rotating Machinery
Using Deep Learning Network Training 3. Towards PEAR: a Benchmark Dataset
for Population-based SHM 4. The Use of Machine Learning in Improved
Hydrostatic Load Prediction for Inland Waterways Navigation Infrastructure
5. Experimental Analysis to Enable Low-Latency Structural Health Monitoring
for Electronics in High-Rate Dynamic Environments 6. Fast-TDA
Implementation for High Rate Dynamic Systems in Noisy Environment and
Introduction to Chaotic System