{"slug": "structure-preserving-uncertainty-quantification-for-generic-dynamics", "title": "Structure-preserving uncertainty quantification for GENERIC dynamics", "summary": "Researchers propose Structure-Preserving Epistemic Neural Networks (S-PENNs), a framework for uncertainty quantification in scientific machine learning models with hard architectural constraints, and instantiate it for GENERIC dynamics. S-PENNs preserve structural constraints by attaching lightweight epinets, ensuring physically admissible realizations, and when combined with split conformal prediction, provide prediction intervals with finite-sample coverage guarantees. Validated on three numerical examples, S-PENNs reduce computational cost by one to three orders of magnitude compared to deep ensembles while maintaining thermodynamic consistency.", "body_md": "arXiv:2608.12624v1 Announce Type: new\nAbstract: Structure-preserving machine learning embeds physical structure directly into model architectures, yet uncertainty quantification (UQ) for such hard-constrained models remains limited because standard UQ methods may violate the encoded admissibility conditions, require architectural modifications, or impose substantial computational costs. In this work, we propose Structure-Preserving Epistemic Neural Networks (S-PENNs), a general framework for UQ in scientific machine learning models with hard architectural constraints, and instantiate it for GENERIC (General Equation for Non-Equilibrium Reversible-Irreversible Coupling) dynamics. S-PENNs preserve the structural constraints of a pretrained model by attaching lightweight epinets to its constrained components, ensuring that every sampled realization remains physically admissible by construction. When applied to GENERIC dynamics, such a proposed framework yields thermodynamically consistent rollouts that preserve the first and second laws. Furthermore, we combine S-PENNs with split conformal prediction as a post-hoc calibration method to produce prediction intervals with finite-sample marginal coverage guarantees. We validate S-PENNs on three numerical examples: a harmonic oscillator coupled to a heat bath and an idealized chemical motor, both governed by ODEs, and a one-dimensional viscoplastic model governed by PDEs. Across all three examples, S-PENNs produce thermodynamically consistent stochastic realizations and well-calibrated prediction intervals while reducing the computational cost by about one to three orders of magnitude compared to deep ensembles. Although the present study focuses on GENERIC dynamics, S-PENNs can be extended more broadly to scientific machine learning models in computational mechanics with either hard or soft constraints.", "url": "https://wpnews.pro/news/structure-preserving-uncertainty-quantification-for-generic-dynamics", "canonical_source": "https://arxiv.org/abs/2608.12624", "published_at": "2026-08-14 04:00:00+00:00", "updated_at": "2026-08-14 04:16:27.799491+00:00", "lang": "en", "topics": ["machine-learning", "artificial-intelligence"], "entities": ["Structure-Preserving Epistemic Neural Networks", "GENERIC dynamics"], "alternates": {"html": "https://wpnews.pro/news/structure-preserving-uncertainty-quantification-for-generic-dynamics", "markdown": "https://wpnews.pro/news/structure-preserving-uncertainty-quantification-for-generic-dynamics.md", "text": "https://wpnews.pro/news/structure-preserving-uncertainty-quantification-for-generic-dynamics.txt", "jsonld": "https://wpnews.pro/news/structure-preserving-uncertainty-quantification-for-generic-dynamics.jsonld"}}