Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling
In physically dominated machining processes, experimental datasets are small, expensive, and material-specific; in this regime, data curation, evaluation design, and the form of physics integration can matter as much as the learning algorithm. Using an abrasive waterjet milling dataset ($n{=}155$, Inconel\,718), we make three methodological contributions. First, we separate physics-based data \emph{cleaning} from statistical \emph{curation} and treat the latter as competing modelling hypotheses rather than silent preprocessing. Second, we find that model rankings from a 15-point hold-out set can be unstable: the single-split winner drops from rank~1 to rank~7 under 10-fold cross-validatio...
| Authors | Sarah Grewe, Jörg Frochte |
|---|---|
| Primary category | cs.LG |
| Published | 8 July 2026 |
| Last updated at source | 8 July 2026 |
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