Interval LSTM and NODE models trained with cascade or joint strategies deliver uncertainty-aware predictions for system identification via interval arithmetic.
23 arXivTemplateA PREPRINT Simon Kristoffersson Lind, Ziliang Xiong, Per-Erik Forssén, and V olker Krüger
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Systematic benchmarking reveals that regression calibration metrics frequently disagree on recalibration quality, with ENCE and CWC identified as more consistent performers.
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Beyond Prediction: Interval Neural Networks for Uncertainty-Aware System Identification
Interval LSTM and NODE models trained with cascade or joint strategies deliver uncertainty-aware predictions for system identification via interval arithmetic.
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Evaluating the Quality of the Quantified Uncertainty for (Re)Calibration of Data-Driven Regression Models
Systematic benchmarking reveals that regression calibration metrics frequently disagree on recalibration quality, with ENCE and CWC identified as more consistent performers.