用傅里叶神经算子预测对流变化，更准更快。
Fourier Neural Operators for Rayleigh-Bénard Convection
- 直接预测时间增量而非全解，提升精度
- 模型仅314k参数，7毫秒推理，1.26MB大小
- 适合需要快速高精度模拟的流体场景
我们提出一种改进的傅里叶神经算子（FNO），用于建模二维瑞利-贝纳德对流，通过预测时间增量而非完整解，实现比标准FNO基线更高的精度。模型紧凑（314千参数，1.26 MB），推理速度快（7毫秒），同时在先前基准测试中保持相当的准确性。我们发现尽管FNO能泛化到更细网格，但其精度仍受限于训练数据的分辨率。
原文摘要 · Abstract (English)
We propose an improved Fourier Neural Operator (FNO) for modeling two-dimensional Rayleigh-Bénard convection by predicting time increments instead of full solutions, achieving higher accuracy than a standard FNO baseline. The resulting model is compact (314k parameters, 1.26 MB) and fast (7 ms inference), while maintaining similar accuracy as demonstrated in previous benchmarks. We show that although FNOs generalize to finer meshes, accuracy remains limited by the resolution of the training data.
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