3rd place Best Paper Award at the 16th Symposium Sensor Data Fusion

29 November 2024

The 16th Symposium Sensor Data Fusion last week brought together industry experts and scientists to explore cutting-edge techniques for transforming data streams into actionable insights. Focused on spatio-temporal integration, redundancy exploitation, and contextual analysis, the conference highlighted applications in defense, aerospace, robotics, and engineering. A big thank you to the organizers, Prof. Dr. Wolfgang Koch und Dr. Felix Govaers, for hosting such an inspiring conference!

As our contribution to the conference, namely 'Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam', we showcased how Physics-Informed Neural Networks (PINNs) can fuse sensor data with underlying physical knowledge to create advanced surrogate models for reinforced concrete beams, enabling hybrid digital twins. This innovative approach bridges classical machine learning with physics, unlocking new potential in structural modeling and real-time applications.

Additionally, we are thrilled to announce that our contribution was honored with the 3rd place Best Paper Award! Grateful for the recognition and proud to share our work with the community.

The article, “Towards a Hybrid Digital Twin: Fusing Sensor Information and Physics in Surrogate Modeling of a Reinforced Concrete Beam,” was by now published in the "2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF).”

Sahin, T., Wolff, D., von Danwitz, M., & Popp, A. (2024). Towards a Hybrid Digital Twin: Physics-Informed Neural Networks as Surrogate Model of a Reinforced Concrete Beam. 2024 Sensor Data Fusion: Trends, Solutions, Applications (SDF), Bonn, Germany, 2024, pp. 1-8, DOI (Open Access) , arXiv