Learning What We Cannot Measure in Engineering Systems
A DSI seminar with Dr. Eleonora Tronci
Assistant Professor of Civil, Urban, and Environmental Engineering
New York University Tandon School of Engineering
November 5, 2026, 3:30-4:30pm
WARF Centennial Seminar Hub (Morgridge Hall 7560)
Modern engineering and physical systems generate increasingly large volumes of data through sensors, monitoring networks, and computational models. However, many of the quantities needed to understand their behavior, assess their condition, and inform decisions remain difficult or impossible to measure directly. Observations are often incomplete, labeled data are scarce, and measurements are influenced by changing operational and environmental conditions. Furthermore, different physical mechanisms can produce similar observable responses, making it challenging to distinguish between accurate predictions and reliable physical interpretations. These limitations raise fundamental questions about what can be learned from data, how knowledge can be transferred across systems, and how uncertainty should be characterized and propagated through predictive models.
In this seminar, Dr. Tronci will present research at the intersection of probabilistic inference and physics-based modeling, aimed at extracting meaningful information from incomplete and indirect observations of complex physical systems. Drawing on applications in civil infrastructure and offshore wind energy, Tronci will discuss how data-driven approaches can identify informative representations of system behavior, infer unmeasured physical quantities, and develop predictive models under varying operational conditions. Tronci will pay particular attention to Gaussian processes, Bayesian estimation, transfer learning, and hybrid modeling strategies that integrate computational simulations with experimental observations. These approaches will be illustrated through applications involving structural response prediction, load estimation, and digital twin development, highlighting their underlying assumptions, capabilities, and limitations.
The seminar will explore the broader challenge of establishing confidence in data-driven models when observations are limited and the underlying physical processes are only partially understood. Tronci will discuss how uncertainty and model assumptions influence the interpretation of monitoring data and the reliability of subsequent decisions.
Bio: Eleonora Maria Tronci is an Assistant Professor in the Department of Civil, Urban and Environmental Engineering at the New York University Tandon School of Engineering. Prior to joining NYU, Dr. Tronci was an Assistant Professor in the Department of Civil and Environmental Engineering at Northeastern University and a Postdoctoral Scholar at Tufts University, where she conducted research on structural health monitoring and digital twins for offshore wind energy systems. She holds a Ph.D. in Civil Engineering and Engineering Mechanics from Columbia University and a Ph.D. in Structural Engineering from Sapienza University of Rome.
Her work integrates statistical learning, Bayesian estimation, system identification, transfer learning, and physics-based modeling to address challenges associated with incomplete measurements, limited training data, and changing environmental and operational conditions. Her research spans structural condition assessment, digital twins, virtual sensing, and uncertainty-informed decision-making, with applications in civil infrastructure, offshore wind energy systems, and intelligent sensing technologies. A central objective of her research is to advance the reliability and interpretability of data-driven models for engineering systems, bridging computational methods with experimental measurements and physical knowledge.