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.
SIAM, Philadelphia (2005)
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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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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.