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Found 4 papers, 1 papers with code
Deep Learning and Spectral Embedding for Graph Partitioning
The embedding phase is trained first by minimizing a loss function inspired by spectral graph theory.
Graph Partitioning and Sparse Matrix Ordering using Reinforcement Learning and Graph Neural Networks
The partitioning quality is compared with partitions obtained using METIS and SCOTCH, and the nested dissection ordering is evaluated in the sparse solver SuperLU.
Deep learning: Extrapolation tool for ab initio nuclear theory
The NCSM and other approaches require an extrapolation of the results obtained in a finite basis space to the infinite basis space limit and assessment of the uncertainty of those extrapolations.
Deep Learning: A Tool for Computational Nuclear Physics
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, meteorology, and other fields of science.
Computational Physics Nuclear Theory
