20 citations · 83 across the 14 of their papers we have counts for
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Symbolic Regression for Interpretable Emulation of Proton Collective Flow in Intermediate-Energy Heavy-Ion Collisions
Nicholas Cox, Xavier Grundler, Bao-An Li
Symbolic regression provides an interpretable machine-learning approach for constructing explicit analytic relations between physical inputs and observables. In this work, we devel…
Universal EOS-Radius Inverse Mappings Govern Precision-Dependent Inference of the Neutron Star Equation of State
Bao-An Li
Bayesian inference of the neutron star (NS) equation of state (EOS) generally assumes that improved observations primarily reduce posterior uncertainties while leaving inferred EOS…
Evolutions of in-medium baryon-baryon scattering cross sections and stiffness of dense nuclear matter from Bayesian analyses of FOPI proton flow excitation functions
Bao-An Li, Wen-Jie Xie
Within a Bayesian statistical framework using a Gaussian Process (GP) emulator for an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model simulator of heavy-ion re…
Imprints of high-density nuclear symmetry energy on the crustal fraction of neutron star moment of inertia
Nai-Bo Zhang, Bao-An Li
The density dependence of nuclear symmetry energy remains the most uncertain aspect of the equation of state (EOS) of supradense neutron-rich nucleonic matter. Imp…
Neural Network Emulation of Flow in Heavy-Ion Collisions at Intermediate Energies
Nicholas Cox, Xavier Grundler, Bao-An Li
Applications of new techniques in machine learning are speeding up progress in research in various fields. In this work, we construct and evaluate a deep neural network (DNN) to be…
Impact of the nuclear equation of state on the formation of twin stars
Nai-Bo Zhang, Bao-An Li
Twin stars-two stable neutron stars (NSs) with the same mass but different radii have long been proposed to appear as a consequence of a possible first-order phase transition in NS…