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Found 7 papers, 2 papers with code
Structure-Preserving Model Reduction for Nonlinear Power Grid Network
We develop a structure-preserving system-theoretic model reduction framework for nonlinear power grid networks.
Model Reduction of Linear Dynamical Systems via Balancing for Bayesian Inference
We introduce Gramian definitions relevant to the inference setting and propose a balanced truncation approach based on these inference Gramians that yield a reduced dynamical system that can be used to cheaply approximate the posterior mean and covariance.
A wavelet-based dynamic mode decomposition for modeling mechanical systems from partial observations
A novel methodology for modeling those classes of dynamical systems is proposed in the present work, using wavelets in conjunction with the input-output dynamic mode decomposition (ioDMD).
Data-driven modeling of power networks
We develop a non-intrusive data-driven modeling framework for power network dynamics using the Lift and Learn approach of \cite{QianWillcox2020}.
Structure-preserving Model Reduction of Parametric Power Networks
We develop a structure-preserving parametric model reduction approach for linearized swing equations where parametrization corresponds to variations in operating conditions.
On the balanced truncation error bound and sign parameters from arrowhead realizations
Balanced truncation and singular perturbation approximation for linear dynamical systems yield reduced-order models that satisfy a well-known error bound involving the Hankel singular values.
Stabilizing discrete empirical interpolation via randomized and deterministic oversampling
Numerical experiments with synthetic and diffusion-reaction problems demonstrate the stability of oversampled empirical interpolation in the presence of noise.
Numerical Analysis
