A U-Net trained on solver trajectories accelerates the energy-adaptive Riemannian conjugate gradient method for rotating Gross-Pitaevskii ground states, saving about 22% of iterations and 14.5% of wall time on average.
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Neural Network Acceleration of Iterative Methods for Nonlinear Schr\"odinger Eigenvalue Problems
A U-Net trained on solver trajectories accelerates the energy-adaptive Riemannian conjugate gradient method for rotating Gross-Pitaevskii ground states, saving about 22% of iterations and 14.5% of wall time on average.