GIOVANNI D'AGNESE

DIF-FNO: Diffeomorphic Fourier Neural Operator with D'Agnese Barrier Loss

Official v1.0.0-sota-proof release for DIF-FNO (Diffeomorphic Fourier Neural Operator) powered by the D'Agnese Topological Barrier Loss. This architecture strictly resolves grid-folding anomalies (det(J_phi) > 0) across non-convex computational domains (NACA 0012, Star, L-Shape,...

DIF-FNO: Diffeomorphic Fourier Neural Operator with D'Agnese Barrier Loss

Official v1.0.0-sota-proof release for DIF-FNO (Diffeomorphic Fourier Neural Operator) powered by the D'Agnese Topological Barrier Loss. This architecture strictly resolves grid-folding anomalies (det(J_phi) > 0) across non-convex computational domains (NACA 0012, Star, L-Shape,...

DIF-FNO: Diffeomorphic Implicit Fourier Neural Operators for Solving PDEs on Irregular Domains

Fourier Neural Operators (FNO) have emerged as a powerful paradigm for learning mappings between infinite-dimensional function spaces, significantly accelerating Partial Differential Equation (PDE) solutions. However, conventional FNOs rely on Fast Fourier Transforms (FFT) over r...