Keywords: Engineering design; MDO; digital twin; system identification  

Organizers:
Ihar Antonau (1,2) – ihar.antonau@tu-braunschweig.de  
Suneth Warnakulasuriya (3) – suneth.warnakulasuriya@tum.de  
Talhah Ansari (3) – talhah.ansari@tum.de  
Bastian Devresse (3) – bastian.devresse@tum.de  
Facundo Airaudo (4) – fairaudo@gmu.edu  
Harbir Antil (5) – hantil@gmu.edu  
Rainald Löhner (4) – rlohner@gmu.edu  
Roland Wüchner (3) – wuechner@tum.de  

Affiliations:
(1) Cluster of Excellence SE²A – Sustainable and Energy-Efficient Aviation, Technische Universität Braunschweig, Germany  
(2) Institute of Structural Analysis, Technical University of Braunschweig, Braunschweig, D-38106, Germany  
(3) Chair of Structural Analysis, Technical University of Munich, Munich, D-80333, Germany  
(4) Center for Computational Fluid Dynamics and Department of Physics and Astronomy, George Mason University, Fairfax, VA 22030, USA  
(5) Center for Mathematics and Artificial Intelligence (CMAI) and Department of Mathematical Sciences, George Mason University, Fairfax, VA 22030, USA  

Abstract:
During their lifecycle, structures undergo changes in their properties due to various factors such as damage, corrosion, and fatigue. As sensor technology and numerical simulation methodologies have matured, the ability to create a digital representation or "Digital Twin" of these complex structures has become increasingly viable. A critical aspect of Digital Twin development is system identification, which entails determining the current state of material properties and localizing points of weakness or deterioration. This process necessitates solving an inverse problem through effective parameterization of the structure, which is typically framed as an optimization challenge.  

In this mini-symposium, we will delve into advanced optimization and system identification techniques pertinent to engineering applications. The discussion will encompass innovative optimization algorithms, including gradient-based methods, genetic algorithms, and Bayesian optimization, which significantly improve the accuracy and efficiency of system identification processes and optimal sensor placement strategies. However, the mini-symposium will not be restricted to these themes; various design methods addressing diverse engineering challenges are welcome as well.  

We invite participants to discuss case studies illustrating the application of these advanced methodologies in various engineering domains, including civil, aerospace, and mechanical engineering. We aim to foster collaboration and advance the use of optimization in leveraging Digital Twin technology for improved structural efficiency, integrity, and safety by discussing current challenges, best practices, and innovative approaches in system identification, while providing participants with insights into proactive maintenance and decision-making processes that enhance sustainability and resilience in engineering design.  

Potential Topics:

• Innovative methods for System Identification in Digital Twins  
• Sensor Measurements and Health Monitoring for Structures  
• Multi-Disciplinary Approaches to Optimization and System Identification in Automotive and Aerospace Engineering 

References:
[1] Löhner, R., Airaudo, F., Antil, H., Wüchner, R., Meister, F., & Warnakulasuriya, S. (2024). High-Fidelity Digital Twins: Detecting and Localizing Weaknesses in Structures. In AIAA SCITECH 2024 Forum (p. 2621).  

[2] Antonau, I., Warnakulasuriya, S., Wüchner, R., Airaudo, F., Löhner, R., Antil, H., & Ansari, T. (2025). Comparison of the First Order Algorithms to Solve System Identification Problems of High-Fidelity Digital Twins. In AIAA SCITECH 2025 Forum (p. 0285).