9^th Intelligent Maintenance Conference

Graph Neural Networks (GNNs) offer a powerful way to analyze complex assets and systems. They have been applied to a wide range of industrial applications, from the analysis of individual components such as bearings or turbofan engines to condition monitoring of large-scale interconnected systems such as power plants, distributed heating networks or energy distribution networks.

In this workshop, we start by exploring the theoretical basics of how Graph Neural Networks (GNNs) facilitate learning in data that is structured as entities and their relationships. Then we'll apply GNNs to industrial applications that benefit from this relational interpretation of the data and learn how GNNs enable model generalization to unseen environments.

The workshop includes a comprehensive introduction to GNNs followed by a hands-on tutorial using an industrial dataset, giving you direct guidance in applying these techniques to solve practical problems.

In the dynamic field of Deep Learning (DL), the ability to adapt models to new, unseen domains is crucial for achieving robust and generalizable performance across a wide range of applications. Designed for practitioners and researchers with a basic understanding of deep learning, this hands-on workshop introduces participants to the concept and techniques of domain adaptation in DL, providing practical skills and knowledge needed to navigate and implement these strategies effectively.