핵심 개념
Incremental Fourier Neural Operator (iFNO) improves training efficiency and generalization performance for solving PDEs.
초록
Fourier Neural Operators (FNO) offer a principled approach to solving challenging PDEs.
Training FNO presents challenges in large-scale applications due to computational intensity and frequency selection.
Incremental Fourier Neural Operator (iFNO) progressively increases frequency modes and resolution during training.
iFNO reduces training time, improves generalization, and requires fewer frequency modes compared to FNO.
Empirical validation across various PDE problems demonstrates the effectiveness of iFNO.
iFNO dynamically selects frequency modes and increases resolution, optimizing training efficiency.
Ablation studies show iFNO outperforms FNO baselines in low-data regimes.
iFNO requires fewer frequency modes during training and achieves better generalization.
iFNO automatically determines the optimal number of frequency modes without pre-determining.
iFNO inference is more efficient and requires fewer parameters than FNO.
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."