A DRX-aware physics-constrained neural network reproduces hot-compression flow stress of Ti–6Al–4Mo–1V–0.1Si with R²≈0.985, but its recrystallization fraction is a soft-prior artifact rather than an independently validated prediction.
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Physics-Informed Residual Deep Learning for Constitutive Modeling of Hot Deformation and Dynamic Recrystallization in a Mo-Rich $\alpha+\beta$ Titanium Alloy
A DRX-aware physics-constrained neural network reproduces hot-compression flow stress of Ti–6Al–4Mo–1V–0.1Si with R²≈0.985, but its recrystallization fraction is a soft-prior artifact rather than an independently validated prediction.