activity
20232026
most citedEmulators for scarce and noisy data: application to auxiliary field diffusion Monte Carlo for the deuteron

12 citations · 27 across the 12 of their papers we have counts for

collaborators

12 papers

nucl-th2026

Bayesian Inference for Extracting Barrier Distributions from Fusion Excitation Functions

Aaron Philip, Pablo Giuliani, Kyle Godbey

Barrier distributions encode rich information about the structure and dynamics of fusing nuclei, but extracting them from experimental fusion cross sections requires an estimate of…

nucl-th2026

Beyond Constant Error: Heteroscedastic Bayesian Model Combination for Modeling Unmeasured Nuclei

B. Knight, S. Lalit, P. Giuliani +4

Experimentally inaccessible regions of the nuclear chart remain a challenge for global models of atomic nuclei to predict. This includes exotic nuclei near particle drip lines, sup…

nucl-th2026

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…

nucl-th2025

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…

physics.ed-ph2025

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…

nucl-th2025★ 11 cited

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…

nucl-th2024

Motivations for Early High-Profile FRIB Experiments

B. Alex Brown, Alexandra Gade, S. Ragnar Stroberg +39

This white paper is the result of a collaboration by those that attended a workshop at the Facility for Rare Isotope Beams (FRIB), organized by the FRIB Theory Alliance (FRIB-TA),…

nucl-th2024★ 1 cited

Genetic Programming for the Nuclear Many-Body Problem: a Guide

Illya Bakurov, Pablo Giuliani, Kyle Godbey +3

Genetic Programming is an evolutionary algorithm that generates computer programs, or mathematical expressions, to solve complex problems. In this Guide, we demonstrate how to use…

nucl-th2024

Model orthogonalization and Bayesian forecast mixing via Principal Component Analysis

Pablo Giuliani, Kyle Godbey, Vojtech Kejzlar +1

One can improve predictability in the unknown domain by combining forecasts of imperfect complex computational models using a Bayesian statistical machine learning framework. In ma…

nucl-th2024★ 12 cited

Emulators for scarce and noisy data: application to auxiliary field diffusion Monte Carlo for the deuteron

Rahul Somasundaram, Cassandra L. Armstrong, Pablo Giuliani +3

The validation, verification, and uncertainty quantification of computationally expensive theoretical models of quantum many-body systems require the construction of fast and accur…

physics.comp-ph2023★ 1 cited

ROSE: A reduced-order scattering emulator for optical models

Daniel Odell, Pablo Giuliani, Kyle Beyer +6

A new generation of phenomenological optical potentials requires robust calibration and uncertainty quantification, motivating the use of Bayesian statistical methods. These Bayesi…

nucl-th2023★ 2 cited

Normalizing Flows for Bayesian Posteriors: Reproducibility and Deployment

Yukari Yamauchi, Landon Buskirk, Pablo Giuliani +1

We present a computational framework for efficient learning, sampling, and distribution of general Bayesian posterior distributions. The framework leverages a machine learning appr…