Data Science in Engineering, Vol. 11
Description
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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Additional editions
Persons
Thomas Matarazzo, François Hemez, Eleonora Maria Tronci, Austin Downey
Content
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
