Infinite-dimensional bilinear and stochastic balanced truncation with error bounds
Provides theoretical error bounds for model reduction of infinite-dimensional bilinear and stochastic systems, addressing a gap in control theory.
Extended balanced truncation to infinite-dimensional bilinear and stochastic systems, proving error bounds and convergence using Hilbert space techniques.
Along the ideas of Curtain and Glover, we extend the balanced truncation method for infinite-dimensional linear systems to bilinear and stochastic systems. Specifically , we apply Hilbert space techniques used in many-body quantum mechanics to establish error bounds for the truncated system and prove convergence results. The functional analytic setting allows us to obtain mixed Hardy space error bounds for both finite-and infinite-dimensional systems, and it is then applied to the model reduction of stochastic evolution equations driven by Wiener noise.