  The Quadrature-based Balanced Truncation (QuadBT) framework of
arXiv:2104.01006 is a "non-intrusive" reformulation of balanced truncation; a
classical projection-based model-order reduction technique for linear systems.
QuadBT is non-intrusive in the sense that it builds approximate balanced
reduced-order models entirely from system response data (e.g., transfer
function measurements) without the need to reference an explicit state-space
realization of the underlying full-order model. In this work, we generalize and
extend QuadBT to other types of balanced truncation model reduction. Namely, we
develop non-intrusive implementations for balanced stochastic truncation,
positive-real balanced truncation, and bounded-real balanced truncation. We
show that the data-driven construction of these balanced reduced-order models
requires sampling certain spectral factors associated with the system of
interest. Numerical examples are included in each case to validate our
approach.
