Fine-tuning a neural preconditioner with a differentiable FGMRES loss on principal angles cuts average FGMRES iterations roughly tenfold on mixed-dimensional 3D-1D problems.
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Neural Preconditioning via Krylov Subspace Geometry
Fine-tuning a neural preconditioner with a differentiable FGMRES loss on principal angles cuts average FGMRES iterations roughly tenfold on mixed-dimensional 3D-1D problems.