Nuclear level density is important for evaluating nuclear reaction processes, but its
microscopic investigation is not frequently performed. The present work applies a recently …

We propose a distributed-memory parallel algorithm for computing some of the algebraically
smallest eigenvalues (and corresponding eigenvectors) of a large, sparse, real symmetric …

We investigate the phase transition of the dodecahedron model on the square lattice. The
model is a discrete analog of the classical Heisenberg model, which has continuous O (3) …

This paper introduces a novel method for eigenvalue computation using a distributed
cooperative neural network framework. Unlike traditional techniques that face scalability …

The determination of three-dimensional positions and velocities of stars based on the
observations collected by a space telescope suffers from the uncertainty of random as well …

The ESSEX project has investigated programming concepts, data structures, and numerical
algorithms for scalable, efficient, and robust sparse eigenvalue solvers on future …

In recent years, a vast amount of data has been accumulated across various fields in
industry and academia, and with the rise of artificial intelligence and machine learning …

Contour integration schemes are a valuable tool for the solution of difficult interior
eigenvalue problems. However, the solution of many large linear systems with multiple right …

In recent years, contour-based eigensolvers have emerged as a standard approach for the
solution of large and sparse eigenvalue problems. Building upon recent performance …

This paper considers computing interior singular triplets corresponding to the singular
values in some interval. Based on the concept of the complex moment-based parallel …