The fast and the fewer
ORAL
Abstract
We will discuss the application of dimensionality reduction techniques to speed up computations and perform model forecast combination for Bayesian Uncertainty Quantification within nuclear physics. We use the same overarching framwork to both identify reduced coordinates for building single model emulators through model order reduction, and to combine groups of models within the Bayesian Model Mixing approach. Both directions are quite important for fostering the experiment/observaiton/theory cycle with well quantified uncertainties.
*Facility for Rare Isotope Beams
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Publication: Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis (May 2024)
Towards accelerated nuclear-physics parameter estimation from binary neutron star mergers: Emulators for the Tolman-Oppenheimer-Volkoff equations (May 2024)
ROSE: A reduced-order scattering emulator for optical models (April 2024)
Emulators for scarce and noisy data: application to auxiliary field diffusion Monte Carlo for the deuteron (April 2024)
Presenters
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Pablo G Giuliani
- Facility for Rare Isotopes Beams
- Facility for Rare Isotope Beams