MS041A Physics-Informed Machine Learning for Structural Health Monitoring: Emerging Trends and Open Issues I
MS Corresponding Organizer:
Dr.
Alberto Barontini
(
University of Minho, ISISE, ARISE
, Italy
)
Chaired by:
Dr. Alberto Barontini (University of Minho, ISISE, ARISE , Italy)
Dr. Alberto Barontini (University of Minho, ISISE, ARISE , Italy)
Scheduled presentations:
-
Physics-informed machine learning surrogate for applications in structural health monitoring and development of digital twins of wind turbines
-
Federated Physics-Informed Machine Learning for Ultrasonic Structural Health Monitoring of Aircraft Structures
-
Efficient Active Learning for Sparse Gaussian Process Classifiers in SHM
-
Virtual Concrete Lab: A Multiscale Computational Framework for Early Damage Assessment through Coda Signal Analysis
-
Physics-Augmented Neural Networks for Constitutive Modeling: Toward an Application for Structural Health Monitoring
-
Enhancing Offshore Wind Turbine Health Monitoring through a Hybrid Approach of Reduced Order Modelling and Machine Learning
