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This paper proposes Structure-Preserving Epistemic Neural Networks (S-PENNs), a framework for uncertainty quantification in scientific machine learning models with hard architectural constraints, instantiated for GENERIC dynamics to ensure thermodynamically consistent rollouts and calibrated prediction intervals with reduced computational cost.