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.
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2026 2verdicts
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GeoFunFlow-3D reduces pressure-field RRMSE to 0.0215 on industrial 3D datasets by combining flow matching with physics-guided components that target spectral bias and localized shock structures.
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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.
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GeoFunFlow-3D: A Physics-Guided Generative Flow Matching Framework for High-Fidelity 3D Aerodynamic Inference over Complex Geometries
GeoFunFlow-3D reduces pressure-field RRMSE to 0.0215 on industrial 3D datasets by combining flow matching with physics-guided components that target spectral bias and localized shock structures.