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.
Learning nonlinear oper- ators via DeepONet based on the universal approximation theorem of operators.Nature machine intelligence, 3(3):218–229, 2021
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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.