The efficient and accurate calculation of how ionic quantum and thermal fluctuations impact
the free energy of a crystal, its atomic structure, and phonon spectrum is one of the main …

Distributed deep learning (DL) has become prevalent in recent years to reduce training time
by leveraging multiple computing devices (eg, GPUs/TPUs) due to larger models and …

Federated learning is an emerging distributed machine learning framework for privacy
preservation. However, models trained in federated learning usually have worse …

We report on Bayesian estimation of the radius, mass, and hot surface regions of the
massive millisecond pulsar PSR J0740+ 6620, conditional on pulse-profile modeling of …

In recent years our understanding of the dense matter equation of state (EOS) of neutron
stars has significantly improved by analyzing multimessenger data from radio/X-ray pulsars …

We report Bayesian inference of the mass, radius, and hot X-ray emitting region properties—
using data from the Neutron Star Interior Composition ExploreR (NICER)—for the brightest …

We report on Bayesian parameter estimation of the mass and equatorial radius of the
millisecond pulsar PSR J0030+ 0451, conditional on pulse-profile modeling of Neutron Star …

MPI for Python (mpi4py) has evolved to become the most used Python binding for the
message passing interface (MPI). We report on various improvements and features that …

Numerical solutions of partial differential equations enable a broad range of scientific
research. The Dedalus project is a flexible, open-source, parallelized computational …

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal
dynamics of high dimensional and reduced order complex systems using Reservoir …