212 citations · 506 across the 17 of their papers we have counts for
17 papers
Deep Learning and Spectral Embedding for Graph Partitioning
Alice Gatti, Zhixiong Hu, Tess Smidt +2
We present a graph bisection and partitioning algorithm based on graph neural networks. For each node in the graph, the network outputs probabilities for each of the partitions. Th…
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
Alice Gatti, Zhixiong Hu, Tess Smidt +2
We present a novel method for graph partitioning, based on reinforcement learning and graph convolutional neural networks. Our approach is to recursively partition coarser represen…
Deep learning: Extrapolation tool for ab initio nuclear theory
Gianina Alina Negoita, James P. Vary, Glenn R. Luecke +8
Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The…
Ab Initio No Core Shell Model with Leadership-Class Supercomputers
James P. Vary, Robert Basili, Weijie Du +9
Nuclear structure and reaction theory is undergoing a major renaissance with advances in many-body methods, strong interactions with greatly improved links to Quantum Chromodynamic…
Deep Learning: A Tool for Computational Nuclear Physics
Gianina Alina Negoita, Glenn R. Luecke, James P. Vary +6
In recent years, several successful applications of the Artificial Neural Networks (ANNs) have emerged in nuclear physics and high-energy physics, as well as in biology, chemistry,…
A Model Order Reduction Algorithm for Estimating the Absorption Spectrum
Roel Van Beeumen, David B. Williams-Young, Joseph M. Kasper +3
The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to th…