研究会 (2026 年 02 月 28 日)

Abstract:

   Modern engineering and scientific discovery increasingly rely on the
   ability to model complex, nonlinear dynamical systems. While traditional
   first-principles models offer interpretability, they often fail to capture
   unmodeled dynamics. Conversely, pure black-box machine learning models
   excel at data fitting but often lack physical consistency and fail in
   long-term forecasting.
   This seminar presents a unified modeling framework designed to bridge
   this gap. By integrating partial physical knowledge with corrective
   data-driven components (such as sparse basis functions or kernel methods),
   we achieve models that are both physically grounded and high-performing.
   The proposed methodology shifts from classical one-step-ahead identification
   to a multi-step framework that minimizes cumulative error over extended
   horizons, ensuring reliability for control and forecasting applications.
   To capture missing dynamics without loosing interpretability, we introduce
   a sparse augmentation strategy that isolates residual effects through
   theoretical bounds and sparsity recovery. This approach is further
   hardened against the irregularities of real-world data, such as missing
   samples and aggregated measurements, by deriving robust estimation bounds
   for non-uniform observations. The effectiveness of these methods is
   demonstrated through diverse applications, ranging from spacecraft inertia
   identification and chemical reactors to ecological population dynamics.

   Bio: Cesare received the B.Sc. degree in Computer Engineering and the
   M.Sc. degree (cum laude) in Computer Engineering from Politecnico di
   Torino, Italy, where he also completed his Ph.D. in Electrical,
   Electronics, and Communication Engineering in early 2026. In 2024,
   he was a Visiting Scholar at The Pennsylvania State University.
   He is currently a postdoctoral research fellow at the Institute of
   Electronics, Computer and Telecommunication Engineering of the Italian
   National Research Council (CNR-IEIIT). He is a member of the IEEE
   committee on System Identification and Adaptive Control. His research
   interests include system identification, physics-based modeling, machine
   learning, filtering/estimation, and optimization.