PINN-AFE uses multi-head attention and input convex networks to solve Monge-Ampère equations with claimed accuracy, efficiency, and extensions to image enhancement and medical registration.
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2026 2verdicts
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Establishes optimal C^{1,α} estimates for p > n and log-Lipschitz continuity under the Lorentz condition f ∈ L^{n,1} for degenerate fully nonlinear elliptic equations with L^p data.
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Physics-Informed Neural Networks with Attention Feature Expansion for Monge-Amp\`ere Equations
PINN-AFE uses multi-head attention and input convex networks to solve Monge-Ampère equations with claimed accuracy, efficiency, and extensions to image enhancement and medical registration.
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A priori estimates for solutions of degenerate fully nonlinear elliptic equations with $L^p$ data
Establishes optimal C^{1,α} estimates for p > n and log-Lipschitz continuity under the Lorentz condition f ∈ L^{n,1} for degenerate fully nonlinear elliptic equations with L^p data.