Combined State and Parameter Reduction

This work investigates two complementary methods for combined state
and parameter reduction of nonlinear systems. First, a system-theoretic
approach using empirical gramians, and second, an iterative method utilizing
the greedy algorithm. The presented methods are applied in the context
of connectivity analysis of functional neuroimaging data, in which these
nonlinear model reduction techniques are demonstrated to accelerate the
solution of many-query problems enabling the data-driven exploration of
more complex neuronal networks, for example in the human brain.