DREAM enables exact-gradient Bayesian calibration of nuclear models via offline SVD emulation of parameter-dependent operators, demonstrated by rapid HMC convergence on an 18-parameter CDCC analysis of d+58Ni scattering.
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Bayesian sampling of ~1M EDF parameter sets combined with subspace-projected CDFT shows that statistical uncertainties bring deformed nuclei 150Nd and 150Sm into agreement with data while near-spherical 136Xe and 136Ba remain outside the predicted bands.
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High-Dimensional Bayesian Calibration of Expensive Nuclear Models with Differentiable Emulation
DREAM enables exact-gradient Bayesian calibration of nuclear models via offline SVD emulation of parameter-dependent operators, demonstrated by rapid HMC convergence on an 18-parameter CDCC analysis of d+58Ni scattering.
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Statistical uncertainty quantification for multireference covariant density functional theory
Bayesian sampling of ~1M EDF parameter sets combined with subspace-projected CDFT shows that statistical uncertainties bring deformed nuclei 150Nd and 150Sm into agreement with data while near-spherical 136Xe and 136Ba remain outside the predicted bands.