Data-Driven Balancing of Linear Dynamical Systems

We present a novel reformulation of balanced truncation, classical model reduction method. The principal innovation that we introduce comes through the use system response data has been either measured or computed, without reference to any prescribed realization original model. Data are represented by sampled values transfer function impulse corresponding discuss parallels our approach bears with Loewner framework, another popular data-driven illustrate numerically in both continuous-time and discrete-time cases.