A variational framework for computing Wannier functions using dictionary learning

DOI10.1016/J.JCP.2021.110793OpenAlexW3211988902MaRDI QIDQ2133725

Bradley Magnetta, Vidvuds Ozoliņš

Publication date: 5 May 2022

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jcp.2021.110793

zbMATH Keywords

optimizationvariational principlemachine learningWannier functionsdictionary learningdata-driven

Mathematics Subject Classification ID

Group theory and generalizations (20-XX) Linear algebraic groups and related topics (20Gxx) Other groups of matrices (20Hxx)

Cites Work

• Unnamed Item
• A feasible method for optimization with orthogonality constraints
• Variational training of neural network approximations of solution maps for physical models
• The Deep Ritz Method: a deep learning-based numerical algorithm for solving variational problems
• The Geometry of Algorithms with Orthogonality Constraints
• Compressed modes for variational problems in mathematics and physics

This page was built for publication: A variational framework for computing Wannier functions using dictionary learning