Comparison of the First Order Algorithms to Solve System Identification Problems of High-Fidelity Digital Twins.

This work focuses on solving the system identification problems of the high-fidelity digital twin based on the measured data via an optimization process. The cost function formulation is based on the aggregated error between measured and computed displacements in different locations. The work aims to test different optimization algorithms with the Vertex Morphing regularization technique. The Nesterov accelerated gradient method is reviewed, studied on a representative structural benchmark, and compared to well-known base methods, such as the steepest descent method. By implementing this advanced methodology, practitioners can achieve timely and accurate assessments of structural integrity, leading to better-informed decision-making regarding maintenance and safety protocols. This research contributes to the ongoing advancement of Digital Twin technology, promoting safer and more efficient infrastructure management.

Reference: 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. American Institute of Aeronautics and Astronautics.

Antonau, I., Warnakulasuriya, S., Ansari, T., Wüchner, R., Löhner, R., Airaudo, F., & Antil, H. (2025). Advancing high-fidelity digital twin technology for structural health monitoring. In 13th International Conference on Structural Health Monitoring of Intelligent Infrastructure.

Antonau, I., Warnakulasuriya, S., Ansari, T., Airaudo, F., Antil, H., Löhner, R., & Wüchner, R. (2025). Formulation and regularization of the optimization problem for adjoint-based digital twin construction. In 16th World Congress on Structural and Multidisciplinary Optimization (WCSMO 2025).