ERC Advanced Grant · 2019
Back to the roots of data-driven dynamical system identification
To obtain data-driven dynamic models for simulation, prediction, monitoring, classification or control tasks, in applications e.g. in Industry 4.0 and eHealth, most identification methods ‘solve’ an optimization problem, relying on some nonlinear iterative algorithm. Undeniably, too many heuristics prevail: What do we mean by ‘solved’? Where did the algorithm converge to? Is the model globally optimal, unique and reproducible? To tackle these scientific deficiencies, we design a framework to deal with inexact data. We solve a longstanding open problem of least squares optimality in system identification: for polynomial dynamical models, the optimal model derives from an eigenvalue problem.…
From the public funding record at EU CORDIS. Describes the funded project, not the reviews below.