Author

Francesco Regazzoni

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#machine learning Preprint Sep 2026

Optimal Transport Dropout for Structured Predictive Uncertainty

Deterministic neural networks and neural operators provide point predictions with no intrinsic measure of reliability. Yet, predictive uncertainty may stem from irreducible outcome variability, finite data, or limitations of the chosen model class. Monte Carlo dropout offers a computationally convenient way to construc...

Giacomo Lorenzon, Francesco Regazzoni · 0 citations

#machine learning Open access Mar 2026

Hyperelastic constitutive model discovery with differentiable finite elements and structure-preserving neural networks

The discovery of constitutive laws from experimentally accessible measurements is a central problem in nonlinear computational mechanics. Many data-driven constitutive identification approaches rely either on paired strain–stress data or on full-field displacement measurements, both of which are difficult to obtain i...

Francesco Regazzoni · Computational Mechanics · 2 citations · ⚡1