Develops a symmetric Hermite quadrature-based balanced truncation algorithm for learning linear dynamical systems from transfer function and derivative data while preserving Hermiticity and asymptotic stability.
Data-driven balancing of linear dynamical systems,
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A dynamic subspace method parameterizes low-dimensional bases as geodesic paths on the Grassmannian to track evolving physics in nonlinear systems, achieving higher accuracy than static approximations at the same rank.
A data-driven reformulation of position-velocity balanced truncation for second-order systems that produces reduced models with generalized proportional damping whose coefficients are inferred from data by least-squares.
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Symmetric Hermite quadrature-based balanced truncation for learning linear dynamical systems from derivative data
Develops a symmetric Hermite quadrature-based balanced truncation algorithm for learning linear dynamical systems from transfer function and derivative data while preserving Hermiticity and asymptotic stability.
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A Dynamic Subspace Approach for Low-rank Approximation of Large-scale Nonlinear Systems
A dynamic subspace method parameterizes low-dimensional bases as geodesic paths on the Grassmannian to track evolving physics in nonlinear systems, achieving higher accuracy than static approximations at the same rank.
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Data-driven balanced truncation for second-order systems with generalized proportional damping
A data-driven reformulation of position-velocity balanced truncation for second-order systems that produces reduced models with generalized proportional damping whose coefficients are inferred from data by least-squares.