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Deep Bayesian Approaches for Interpretable Nonlinear Structural System Identification
System identification, namely the inference of mathematical representations of dynamical systems from measured data, is fundamental to engineering diagnostics, prognostics, and decision-making. Bayesian filtering offers a principled framework for probabilistic state estimation but traditionally reli
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