A physics-informed neural representation is learned from safe data to support distributional hypothesis testing for dynamical instability in stochastic DAE systems without repeated simulations.
arXiv preprint arXiv:2212.02742 , year=
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Disagreement measures from label flipping in IDT ensembles underperform loss-based drift detectors in streaming tabular data due to the limited plasticity of tree models.
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Learning to Test: Physics-Informed Representation for Dynamical Instability Detection
A physics-informed neural representation is learned from safe data to support distributional hypothesis testing for dynamical instability in stochastic DAE systems without repeated simulations.
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Pitfalls of Unlabeled Disagreement-Based Drift Detection in Streaming Tree Ensembles
Disagreement measures from label flipping in IDT ensembles underperform loss-based drift detectors in streaming tabular data due to the limited plasticity of tree models.