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Sponsored AI Networking Cookbook: Practical recipes for AI-assisted network automation and development AI Networking Cookbook: Practical recipes for AI-assisted network automation and development

Author

Aswin Karkadakattil

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4 papers indexed here

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Open access Aug 2026

A physics-guided unified damage index framework for explainable milling anomaly detection using interval-wise MTConnect multi-sensor fusion

Reliable anomaly detection in precision milling is essential for maintaining dimensional accuracy, tool life, surface integrity and component reliability. Conventional data-driven approaches often average sensor signals over entire machining records and rely on black-box machine learning models, reducing physical i...

Aswin Karkadakattil · 0 citations
Review Sep 2026

Scientific reliability of physics-informed foundation models for engineering: a critical review, evidence-based assessment framework, and future research roadmap

Overall, SRAF provides an evidence-driven basis for developing engineering AI systems that are physically consistent, verifiable, uncertainty-aware, reproducible, computationally efficient, and suitable for industrial deployment.

Aswin Karkadakattil · 0 citations
Open access Jul 2026

Genetic algorithm–optimized loss balancing in physics-informed neural networks for manufacturing digital twin applications

This study addresses a key limitation in physics-informed neural networks (PINNs), namely the reliance on manually selected or heuristically tuned loss weights governing the balance between data fidelity, physics residuals and boundary constraints. Improper weighting often leads to instability, poor reproducibility...

Aswin Karkadakattil · 0 citations
Jul 2026

RAPIL: a reliability-aware physics-guided learning framework for robust density prediction and generalization assessment in laser powder bed fusion

The Reliability-Aware Physics-Guided Learning (RAPIL) framework provides an interpretable, reliability-aware framework for relative density prediction across heterogeneous LPBF datasets while highlighting the remaining challenges associated with machine-dependent variability and model transferability.

Aswin Karkadakattil · 0 citations

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