Author

Prashil S. Joshi

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Preprint Sep 2026

From Local Atomic Motifs to Thermodynamic State: An Interpretable Physics-Informed Framework for Cu-Zr Metallic Glasses

Machine-learning models that relate local atomic structure to the thermodynamic state of metallic glasses typically assess physical consistency after training rather than enforcing it during learning. Here, we develop a multi-task physics-informed neural network (PINN) that predicts temperature directly from Voronoi-mo...

Prashil S. Joshi · 0 citations

Preprint Sep 2026

Physics-guided inverse design of Co-based superalloys using machine learning and multi-objective optimization for enhanced $\gamma'$ solvus temperature

The discovery of next-generation Co-based superalloys with improved high-temperature stability is hindered by the vast compositional design space and complex interactions among alloying elements governing gamma-prime phase stability. This study presents a physics-informed machine learning framework for the inverse desi...

Prashil S. Joshi · 0 citations

Preprint Jul 2026

Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy

This model accurately reproduced temperature-dependent flow curves, DRX kinetics, and Zener-Hollomon relationships, while maintaining physically consistent constitutive behavior, demonstrating that physics-informed deep learning provides a robust and interpretable framework for constitutive modeling.

Prashil S. Joshi, Diksha Mahadule, Rajesh K.Khatirkar · 0 citations