For small AWJM process data, treating statistical curation as competing hypotheses, using multi-fold evaluation, and residual physics with GPs yields more stable rankings and calibrated uncertainty than single-split pure ML.
C.: On over-fitting in model selection and subsequent selection bias in performance evaluation
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Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling
For small AWJM process data, treating statistical curation as competing hypotheses, using multi-fold evaluation, and residual physics with GPs yields more stable rankings and calibrated uncertainty than single-split pure ML.