IRNO augments neural operators with learned fixed-point iterative refinement modules and a progressive spectral loss, achieving up to 56% error reduction on turbulent flow and large drops in high-frequency normalized errors on active matter.
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MscaleFNO learns mappings from oscillatory media to wavefields for Helmholtz inverse problems and pairs it with diffusion regularization for partial-aperture 2D reconstructions.
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Iterative Refinement Neural Operators are Learned Fixed-Point Solvers: A Principled Approach to Spectral Bias Mitigation
IRNO augments neural operators with learned fixed-point iterative refinement modules and a progressive spectral loss, achieving up to 56% error reduction on turbulent flow and large drops in high-frequency normalized errors on active matter.
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Multiscale Fourier Neural Operator for Inverse Wave Scattering in Highly Oscillatory Media
MscaleFNO learns mappings from oscillatory media to wavefields for Helmholtz inverse problems and pairs it with diffusion regularization for partial-aperture 2D reconstructions.