Combined state and parameter reduction for nonlinear systems with an application in neuroscience

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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.