改进多相流预测模型，提升精度并聚焦关键区域
Feature-Modulated UFNO for Improved Prediction of Multiphase Flow in Porous Media
- 用特征调制层分离标量输入与空间特征，避免冗余信号干扰
- 在关键区域加权损失函数，使误差更小，气体饱和度MAE降21%
- 适合需要高精度模拟地下多相流动的地质建模场景
UFNO通过引入并行的UNet路径，增强了傅里叶神经算子（FNO）对高低频成分的保留能力，提升了多孔介质中多相流的预测精度。然而，其将标量输入（如温度、注入速率）视为全域分布场，导致在频率域中处理重复常量信号，效率低下。同时，标准损失函数未考虑误差敏感性的空间差异，限制了在高物理重要区域的表现。本文提出UFNO-FiLM，引入两个创新：首先，采用特征式线性调制（FiLM）层，将标量输入与空间特征解耦，使模型能调节特征图而不引入常量信号至傅里叶变换；其次，使用空间加权损失函数，优先优化关键区域的学习。在地下多相流实验中，相比UFNO，气体饱和度的平均绝对误差（MAE）降低21%，验证了方法的有效性。
原文摘要 · Abstract (English)
The UNet-enhanced Fourier Neural Operator (UFNO) extends the Fourier Neural Operator (FNO) by incorporating a parallel UNet pathway, enabling the retention of both high- and low-frequency components. While UFNO improves predictive accuracy over FNO, it inefficiently treats scalar inputs (e.g., temperature, injection rate) as spatially distributed fields by duplicating their values across the domain. This forces the model to process redundant constant signals within the frequency domain. Additionally, its standard loss function does not account for spatial variations in error sensitivity, limiting performance in regions of high physical importance. We introduce UFNO-FiLM, an enhanced architecture that incorporates two key innovations. First, we decouple scalar inputs from spatial features using a Feature-wise Linear Modulation (FiLM) layer, allowing the model to modulate spatial feature maps without introducing constant signals into the Fourier transform. Second, we employ a spatially weighted loss function that prioritizes learning in critical regions. Our experiments on subsurface multiphase flow demonstrate a 21\% reduction in gas saturation Mean Absolute Error (MAE) compared to UFNO, highlighting the effectiveness of our approach in improving predictive accuracy.
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