Mechanism failed
2026
Signed-Distance Ambient FNO
Embed every variable-domain sample into one fixed ambient hyperrectangle and append its signed-distance function as an additional channel to the operator input. Deterministically extend fields outside the physical domain, resample them onto a shared latent grid, apply standard Fourier layers, then interpolate and mask the output on the requested target discretization. The network learns the operator rather than a separate geometry encoder, so the same weights can be used across shapes and mesh…
Paper: ADEx-FNO: A Unified Ambient-Domain Framework for Fourier Neural Operators on Varying Geometries
arXiv:2608.08608