S-PENNs inject epistemic uncertainty into hard-constrained GENERIC neural networks blockwise, preserving energy conservation and nonnegative entropy production in every sample, and use split conformal prediction to calibrate the intervals.
SympNets: Intrinsic structure- preserving symplectic networks for identifying Hamiltonian systems.Neural Networks, 132:166–179, 2020
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Structure-preserving uncertainty quantification for GENERIC dynamics
S-PENNs inject epistemic uncertainty into hard-constrained GENERIC neural networks blockwise, preserving energy conservation and nonnegative entropy production in every sample, and use split conformal prediction to calibrate the intervals.