Physics-informed Fourier neural operators recover plasmoid formation in sparse SRRMHD vortex data where data-only models fail, and transformer operators approximate AMR jet evolution, marking first reported uses in these relativistic MHD settings.
Principled approaches for extending neural architectures to function spaces for operator learning
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Fourier Neural Operators lack reliable zero-shot resolution equivariance on Darcy flow; direct inference at higher resolution can underperform low-resolution inference plus upsampling.
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Learning Neural Operator Surrogates for the Black Hole Accretion Code
Physics-informed Fourier neural operators recover plasmoid formation in sparse SRRMHD vortex data where data-only models fail, and transformer operators approximate AMR jet evolution, marking first reported uses in these relativistic MHD settings.