Combined State and Parameter Reduction
for Nonlinear Systems with an Application in Neuroscience
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Beschreibung
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.
Autor*in
Christian Himpe
Themen in »Combined State and Parameter Reduction«
Balanced Truncation Combined State and Parameter Reduction Cross Gramian Dynamic Causal Modeling Empirical Gramians Functional Neuroimaging Greedy Algorithm Matlab Model Order Reduction Model Reduction Numerische Mathematik Systems Neuroscience