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The thermodynamic relation between pressure and density (i.e. the equation of state) of cold supranuclear matter is critical in describing neutron stars, yet it remains one of the largest uncertainties in nuclear physics. The extraction of…
Extracting Times of Arrival from pulsar radio signals depends on the knowledge of the pulsars pulse profile and how this template is generated. We examine pulsar template generation with Bayesian methods. We will contrast the classical…
The forward problems of pattern formation have been greatly empowered by extensive theoretical studies and simulations, however, the inverse problem is less well understood. It remains unclear how accurately one can use images of pattern…
Several new proton-proton parity violation experiments are presently either being performed or are being prepared for execution in the near future. Similarly, a new measurement of the parity-violating gamma-ray asymmetry in polarized…
Describing the interactions of water molecules is one of the most common, yet critical, tasks in molecular dynamics simulations. Because of its unique properties, hundreds of attempts have been made to construct an ideal interaction…
High-quality nanomechanical oscillators can sensitively probe force, mass, or displacement in experiments bridging the gap between the classical and quantum domain. Dynamics of these stochastic systems is inherently determined by the…
Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the available limited data…
The problem of assigning probability distributions which objectively reflect the prior information available about experiments is one of the major stumbling blocks in the use of Bayesian methods of data analysis. In this paper the method of…
Theoretical uncertainties of various types are discussed for the nucleon-deuteron elastic scattering observables at the incoming nucleon laboratory energies up to 200 MeV. We are especially interested in the statistical errors arising from…
Inverse problems are ubiquitous in the sciences and engineering. Two categories of inverse problems concerning a physical system are (1) estimate parameters in a model of the system from observed input-output pairs and (2) given a model of…
There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstrate the use of Bayesian Hidden Physics Models to first…
The complex nature of inertial confinement fusion (ICF) experiments results in a very large number of experimental parameters that are only known with limited reliability. These parameters, combined with the myriad physical models that…
The electromagnetic polarizabilities of the nucleon are fundamental properties that describe its response to external electric and magnetic fields. They can be extracted from Compton-scattering data --- and have been, with good accuracy, in…
Machine learning methods for computational imaging require uncertainty estimation to be reliable in real settings. While Bayesian models offer a computationally tractable way of recovering uncertainty, they need large data volumes to be…
To improve the theoretical prediction power for synthesizing superheavy elements beyond Og, a Bayesian uncertainty quantification method is employed to evaluate the uncertainty of the calculated evaporation residue cross sections (ERCS) for…
Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy and sustainability. To ensure the…
Forthcoming fixed-target coherent elastic neutrino-nucleus scattering experiments aim at measurements with $\cal{O}(\text{tonne})$-scale detectors and substantially reduced systematic and statistical uncertainties. With such high quality…
Over twenty-five years ago, two analyses of the pion-nucleon ($\pi N$) data at low energy (i.e., for pion laboratory kinetic energy $T \leq 100$ MeV) reported on the departure of the extracted scattering amplitudes, corresponding to the two…
Bayesian inference allows the transparent communication of uncertainty in material flow analyses (MFAs), and a systematic update of uncertainty as new data become available. However, the method is undermined by the difficultly of defining…
In this study, we use Rational-Quadratic Neural Spline Flows, a sophisticated parametrization of Normalizing Flows, for inferring posterior probability distributions in scenarios where direct evaluation of the likelihood is challenging at…