Preview and not Published
Data Science in Engineering, 2026, Vol. 9

Data Science in Engineering, 2026, Vol. 9
Proceedings of the 44th IMAC, A Conference and Exposition on Structural Dynamics 2026

Proceedings

Data Science in Engineering, 2026, Vol. 9
Proceedings of the 44th IMAC, A Conference and Exposition on Structural Dynamics 2026

Editors:
 Thomas Matarazzo, United States Military Academy
Franc¸ois Hemez, Lawrence Livermore National Laboratory, Livermore, CA, USA
Eleonora Maria Tronci, New York University
Austin Downey, University of South Carolina

ISBN: 9788743814481 e-ISBN: 9788743814207

Available: July 2026

doi: https://doi.org/10.13052/rp-9788743814207

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
NeRFs, 3D scanning, PINN identification damping, Fluid Dynamics, Neural Operators, Multi-Task Learning, PIML, Soft constraint, Weight tuning, adaptive learning, Acoustic Emission, Anomaly detection, AI-based NDT, Network Science, Graph Theory, IE Models, PBSHM

Chapter 1: Full-Field 3D Structural Dynamics via Neural Radiance Fields
by Thiago F. Ribeiro, Victor H. R. Cardoso, Joao C. W. A. Costa, Moises F. Silva
https://doi.org/10.13052/rp-9788743814207A10


1

Chapter 2: Investigation on the identification of Spatially-Varying Elastic and Damping Properties in Beams and Plates Using Physics-Informed Neural Networks
by Morvan Ouisse, Maxime Auger, Joao Gabriel Oliveira Aveiro, Roberta Tittarelli, Valentin Calisti, Rafael Teloli, Emmanuel Ramasso, Patrice Le Moal
https://doi.org/10.13052/rp-9788743814207A02


1

Chapter 3: Neural Operators for Accelerating Flow Field Predictions in an Empty Duct
by Alan Xavier, Ludovic Renson
https://doi.org/10.13052/rp-9788743814207A03


1

Chapter 4: Shaping a Shock Response Spectrum via Metamaterial Topology Optimization
by Brannon Boyd and Vijaya V. N. Sriram Malladi
https://doi.org/10.13052/rp-9788743814207A04


1

Chapter 5: Physics-based Online Cyber-Physical Neural Network Models for Real-time Hybrid Simulation
by Faisal Malik, Liang Cao, James Ricles, Austin Downey
https://doi.org/10.13052/rp-9788743814207A05


1

Chapter 6: Physics-Informed Machine Learning Part III: Hard-Constraint ODE Method for Structural Dynamics
by Mohsen Gol Zardian, Austin R.J. Downey, Eleonora Maria Tronci, Conor Madden, Daniel Coble, Sina Navidi, Chao Hu
https://doi.org/10.13052/rp-9788743814207A06


1

Chapter 7: Multi-Task Equation Discovery
by S.C. Bee, K. Worden, N. Dervilis, L.A. Bull
https://doi.org/10.13052/rp-9788743814207A07


1

Chapter 8: Physics-Informed Machine Learning Part IV: Weight-Tuned Soft-Constraint Method for Structural Dynamics
by Eleonora Maria Tronci, Austin R. J. Downey, Connor Madden, Mohsen Gol Zardian, Daniel Coble
https://doi.org/10.13052/rp-9788743814207A08


1

Chapter 9: Temperature-driven Unsupervised Damage Identification using Cepstral Features
by Lorenzo Sclafani, Lorenzo Stagi, Silvia Milana, Eleonora M. Tronci
https://doi.org/10.13052/rp-9788743814207A09


1

Chapter 10: Leveraging Agentic AI and LLMs to Enhance Conceptual Understanding of Structural Dynamics
by Krishna Teja Josyula, Vijaya V. N. Sriram Malladi
https://doi.org/10.13052/rp-9788743814207A10

Chapter 11: AI-Enhanced Anomaly Detection of Acoustic Emission Sensor Data via Clustering and Noise Discrimination: A Machine Learning-Based Study
by Ali Bakhtiari, Tonghao Zhang, Antonio De Luca, Nezar Abraham
https://doi.org/10.13052/rp-9788743814207A11


1

Chapter 12: On the Communities of a Population-based SHM Network
by Daniel S. Brennan, Keith Worden
https://doi.org/10.13052/rp-9788743814207A12


1