SpectraNet delivers stable autoregressive PDE rollouts with lower error and 2.3x fewer parameters than FNO by embedding spectral convolutions in a U-Net and training a residual-target block under semigroup-consistency loss.
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GANO is an end-to-end differentiable latent-space optimizer that unifies shape encoding, surrogate prediction, and controllable geometry updates for PDE-governed shape optimization and inversion.
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Bridging Spectral Operator Learning and U-Net Hierarchies: SpectraNet for Stable Autoregressive PDE Surrogates
SpectraNet delivers stable autoregressive PDE rollouts with lower error and 2.3x fewer parameters than FNO by embedding spectral convolutions in a U-Net and training a residual-target block under semigroup-consistency loss.
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Geometry-Aware Neural Optimizer for Shape Optimization and Inversion
GANO is an end-to-end differentiable latent-space optimizer that unifies shape encoding, surrogate prediction, and controllable geometry updates for PDE-governed shape optimization and inversion.