Six mixed training datasets are compared for a U-Net Helmholtz preconditioner; the best mix lets FGMRES solve 512×512 head-CT problems that GMRES and the Stanziola learned optimizer cannot.
Conditional Denoising Diffusion Model-BasedRobustMRImageReconstructionfromHighlyUndersampledData
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Neural operator preconditioning from mixed dataset for the Helmholtz equations: Application to transcranial ultrasound
Six mixed training datasets are compared for a U-Net Helmholtz preconditioner; the best mix lets FGMRES solve 512×512 head-CT problems that GMRES and the Stanziola learned optimizer cannot.