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.
Welcome
Recent Publications
2019
- Dutta A., McKay M., Kopsaftopoulos F., Gandhi F.,. "Rotor fault detection and identification on a hexacopter based on statistical time series methods," Vertical Flight Society 75th Annual Forum & Technology Display, Philadelphia, PA, USA, May 2019. [Publication, Abstract]
2020
- Dutta A., McKay M., Kopsaftopoulos F., Gandhi, F.,. "Time series statistical learning methods for multicopter fault detection and identification," Aeromechanics for Advanced Vertical Flight Technical Meeting, San Jose, CA, USA, January 2020. [Abstract]
2021
- Cherian-Ashe R., Arumbakkam S., Amer A., Kopsaftopoulos F.. "Damage state parametrization for active-sensing SHM via an integrated statistical time series and Bayesian learning framework," AIAA SciTech Forum, AIAA-2021-0302, Virtual Conference, January 2021 [Publication, Abstract]