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.
Principal component analysis in linear systems: Controllability, observability, and model reduction,
5 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 5representative citing papers
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.
citing papers explorer
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Area Scaling of Dynamical Degrees of Freedom in Regularised Scalar Field Theory
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.
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An Efficient Batch Solver for the Singular Value Decomposition on GPUs
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.
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Dimension and model reduction approaches for linear Bayesian inverse problems with rank-deficient prior covariances
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.
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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.
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Control-oriented model reduction for minimizing transient energy growth in shear flows
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.