From the 2 of 9 linked papers with an AI index.
9 papers
Bayesian Inference for Extracting Barrier Distributions from Fusion Excitation Functions
Aaron Philip, Pablo Giuliani, Kyle Godbey
The paper presents a Bayesian machine‑learning approach (AutoBNN) for extracting nuclear barrier distributions from sparse fusion excitation function data, providing calibrated unc…
Beyond Constant Error: Heteroscedastic Bayesian Model Combination for Modeling Unmeasured Nuclei
B. Knight, S. Lalit, P. Giuliani +4
The paper introduces a heteroscedastic Bayesian Model Combination framework to improve uncertainty quantification when combining nuclear energy density functional models, especiall…
Emulating Density Functional Theory Calculations via Empirical Interpolation
Daniel Lay, Pablo Giuliani, Kyle Godbey
Nuclear density functional theory (DFT) is a suitable tool for predicting nuclear ground-state and fission properties. Statistical uncertainty quantification is desirable to make t…
Wavefunction-Based Emulation of Coupled-Channels Scattering with Non-Affinely Parametrized Interactions
M. Catacora-Rios, Kyle Beyer, Pablo Giuliani +3
Physics based emulators offer a fast and reliable replacement for an exact solution of the scattering problem in nuclear physics. Previous work developed a reduced-basis emulator f…
Emulators for Scarce and Noisy Data: Application to Auxiliary-Field Diffusion Monte Carlo for Neutron Matter
Cassandra L. Armstrong, Pablo Giuliani, Kyle Godbey +2
Understanding the equation of state (EOS) of pure neutron matter is necessary for interpreting multimessenger observations of neutron stars. Reliable data analyses of these observa…
Nuclear Beavers
Joshua Wylie, Pablo Giuliani, Kyle Godbey +1
Nuclear physics is a very abstract field with little accessibility for wider audiences, and yet it is a field of physics with far reaching implications for everyday life. The Nucle…