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Author

Milon Selvam Dennison

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#reinforcement learning Dataset Open access Sep 2026

Reproducibility package for "Formulation-holdout validation for reliable machine learning in replicated materials experiments: an AA6061 composite case study"

This record contains the complete reproducibility package supporting the manuscript "Formulation-holdout validation for reliable machine learning in replicated materials experiments: an AA6061 composite case study." The study evaluates 12 independently prepared AA6061 hybrid-composite specimens nested within four reinforcement loadings (0, 5, 10, and 15 wt.%) and contrasts replicate-holdout validation with formulation-holdout validation (FHV). The archive includes the specimen-level dataset, executable Python analysis, pooled out-of-fold predictions, full model-performance audit, Matern-5/2 GPR predictive-interval audit, held-out formulation means, full-data GPR grid, conservative lower-confidence-bound score, uncertainty-ranked candidate formulations, selected next-experiment loadings, programmatically reproduced analysis figures, software requirements, verification checks, and provenance documentation. No synthetic observations, interpolated labels, or data augmentation are used. The archive is intended to reproduce the reported validation and uncertainty analyses and to document the distinction between repeatability-level prediction at known formulations and design-level generalization to unseen formulations.

Obinna Onyebuchi Barah, Milon Selvam Dennison, Akpan Samuel Cletus · 0 citations
#reinforcement learning Dataset Open access Sep 2026

Reproducibility package for "Formulation-holdout validation for reliable machine learning in replicated materials experiments: an AA6061 composite case study"

This record contains the complete reproducibility package supporting the manuscript "Formulation-holdout validation for reliable machine learning in replicated materials experiments: an AA6061 composite case study." The study evaluates 12 independently prepared AA6061 hybrid-composite specimens nested within four reinforcement loadings (0, 5, 10, and 15 wt.%) and contrasts replicate-holdout validation with formulation-holdout validation (FHV). The archive includes the specimen-level dataset, executable Python analysis, pooled out-of-fold predictions, full model-performance audit, Matern-5/2 GPR predictive-interval audit, held-out formulation means, full-data GPR grid, conservative lower-confidence-bound score, uncertainty-ranked candidate formulations, selected next-experiment loadings, programmatically reproduced analysis figures, software requirements, verification checks, and provenance documentation. No synthetic observations, interpolated labels, or data augmentation are used. The archive is intended to reproduce the reported validation and uncertainty analyses and to document the distinction between repeatability-level prediction at known formulations and design-level generalization to unseen formulations.

Obinna Onyebuchi Barah, Milon Selvam Dennison, Akpan Samuel Cletus · 0 citations

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