핵심 개념
Incremental Fourier Neural Operator (iFNO) improves training efficiency and generalization performance for solving PDEs.
통계
Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows.
iFNO reduces total training time while maintaining or improving generalization performance across various datasets.
iFNO demonstrates a 10% lower testing error, using 20% fewer frequency modes compared to FNO, while achieving a 30% faster training.
인용구
"Fourier Neural Operators (FNO) offer a principled approach to solving challenging partial differential equations (PDE) such as turbulent flows."
"iFNO reduces total training time while maintaining or improving generalization performance across various datasets."
"Our method demonstrates a 10% lower testing error, using 20% fewer frequency modes compared to the existing Fourier Neural Operator, while also achieving a 30% faster training."