Welcome to the website of the RPI Intelligent Structural Systems Laboratory (ISSL)! The mission of ISSL is to develop methodologies that will enable the next generation of intelligent, self-aware aerial vehicles that can "feel," "think," and "react". Research on ISSL focuses on advanced data-driven stochastic modeling, identification and learning methods in the face of complex dynamic environments and uncertainty. Our work lies at the intersection of stochastic systems, machine learning and physics-based domain knowledge. Particular emphasis is placed on intelligent structural systems, fault diagnosis and structural health monitoring, and "fly-by-feel" aerial vehicles.

Recent Publications


  1. Ahmed S., Kopsaftopoulos F.P.. "Active Sensing Acousto-Ultrasound SHM via Stochastic Non-stationary Time Series Models," European Workshop on Structural Health Monitoring, pp. 256-266, Springer, Cham, 2023 [Publication, Abstract]
  2. Fan Y., , Kopsaftopoulos F.P.. "Damage State Estimation via Multi-fidelity Gaussian Process Regression Models for Active-Sensing Structure Health Monitoring," European Workshop on Structural Health Monitoring, pp. 267-276, Springer, Cham, 2023 [Publication, Abstract]


  1. Drakoulas, G., Gortsas T., Kokkinos C., Kopsaftopoulos, F.P.. "A Machine Learning Framework for Reduced Order Modeling of Guided Waves Propagation," 13th International Congress on Mechanics HSTAM, 2022 [Publication, Abstract]