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Welcome

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

2026

  1. Stamopoulos C., Huang S., Kopsaftopoulos F.. "Time Series Foundation Model and Stochastic Functional Time Series Models for Dynamic Data-Driven Inference under Varying Operating States," Paper Accepted, Dynamic Data Driven Applications Systems (DDDAS) – InfoSymbiotics and AI, New Brunswick, NJ, USA, November 2026 [Abstract]
  2. Spiliopoulos P., Mishra S., Kopsaftopoulos F.,. "Physics-Informed Thermal Field Reconstruction for Melt-Pool Monitoring in Laser Powder Bed Fusion," Paper Accepted, Dynamic Data Driven Applications Systems (DDDAS) – InfoSymbiotics and AI, New Brunswick, NJ, USA, November 2026 [Abstract]

2025

  1. Huang S., Zhu Z., Zhou P., Kopsaftopoulos F.. "A Functional Time Series Framework for Probabilistic Health Monitoring on a Hexacopter: Experimental Evaluation via a Series of Flight Tests," AIAA SciTech Forum, Orlando, FL, January 2025 [Publication, Abstract]