A parametric data-driven model built with p-AAA reduces relative force estimation error by nearly 38% versus the best non-parametric model while generalizing across load amplitudes and input waveforms.
Structu red barycentric forms for interpolation-based data-drive n re- duced modeling of second-order systems,
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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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Load Identification in Bistable Spacecraft Booms via Parametric Data-Driven Modeling
A parametric data-driven model built with p-AAA reduces relative force estimation error by nearly 38% versus the best non-parametric model while generalizing across load amplitudes and input waveforms.
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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.