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.
Principal component analysis in linear systems: Controllability, observability, and model reduction
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The minimal number of dynamical degrees of freedom in regularised scalar field theory scales with area, governed by the count of distinct normal-mode frequencies below the ultraviolet cutoff.
A new GPU-oriented batch SVD solver based on the one-sided Jacobi method delivers significant speedups over vendor libraries and prior open-source implementations across precisions and matrix shapes.
New dimension and model reduction techniques for linear Bayesian inverse problems with rank-deficient priors, with approximation guarantees and efficiency demonstrations for high-dimensional inference.
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.
POD output projection plus balanced truncation creates reduced-order models that make LMI control synthesis tractable for minimizing transient energy growth in channel flow, outperforming LQR.
Introduces local information operators that separate pointwise visibility from spatial identifiability via linearized Fisher information and sensitivity Gramians in distributed-parameter inverse problems.
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Local Information Operators for Spatial Identifiability in Distributed-Parameter Inverse Problems in Computational Mechanics
Introduces local information operators that separate pointwise visibility from spatial identifiability via linearized Fisher information and sensitivity Gramians in distributed-parameter inverse problems.