Physics-Informed Machine Learning Under Small-Data Constraints: Lessons from Abrasive Waterjet Milling

arXiv:2607.07863 · cs.LG · Submitted 2026-07-08 · Read on arXiv

cs.LG

Submitted: 2026-07-08

Updated: 2026-07-08

Comments: 2 figures, 5 tables, Accepted for publication; to be published in the Proceedings of ICANN 2026, Springer LNCS

Journal ref: Artificial Neural Networks and Machine Learning - ICANN 2026, Lecture Notes in Computer Science, vol. 17093, pp. 462-473, Springer, Cham (2027)

DOI: 10.1007/978-3-032-38404-1_37

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Accuracy — 6× in this paper · explained in 1 episode
• Empirical Coverage — 6× in this paper · explained in 2 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Gaussian Process — 3× in this paper
• Gradient Boosting — 3× in this paper
• Epistemic Uncertainty — 2× in this paper · explained in 9 episodes
• Hybrid Models — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Benchmark — 1× in this paper · explained in 2 episodes
• Conformal Prediction — 1× in this paper · explained in 5 episodes
• Data Augmentation — 1× in this paper · explained in 7 episodes
• Feature Selection — 1× in this paper · explained in 4 episodes
• Hybrid Approach — 1× in this paper · explained in 4 episodes
• Information Leakage — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Loss Function — 1× in this paper · explained in 2 episodes
• Sample Efficiency — 1× in this paper · explained in 4 episodes
• Scaling Laws — 1× in this paper · explained in 4 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Uncertainty Quantification — 1× in this paper · explained in 8 episodes

Sources

• Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning

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