Abstract
Multiphase flow simulation is critical in science and engineering but incurs high computational costs due to complex field discontinuities and the need for high-resolution numerical meshes. While Neural Operators (NOs) offer an efficient alternative for solving Partial Differential Equations (PDEs), they struggle with two core challenges unique to multiphase systems: spectral bias caused by spatial heterogeneity at phase interfaces, and the persistent scarcity of expensive, high-resolution field data. This work introduces the Interface Information Aware Neural Operator (IANO), a novel architecture that mitigates these issues by leveraging readily obtainable interface data (e.g., topology and position). Interface data inherently contains the high-frequency features not only necessary to complement the physical field data, but also help with spectral bias. IANO incorporates an interface-aware function encoding mechanism to capture dynamic coupling, and a geometry-aware positional encoding method to enhance spatial fidelity for pointwise super-resolution. Empirical results across multiple multiphase flow cases demonstrate that IANO achieves significant accuracy improvements (up to $\sim$10\%) over existing NO baselines. Furthermore, IANO exhibits superior generalization capabilities in low-data and noisy settings, confirming its utility for practical, data-efficient $\text{AI}$-based multiphase flow simulations.
Abstract (translated)
多相流模拟在科学和工程领域中至关重要，但由于相界面的复杂场不连续性和对高分辨率数值网格的需求，其计算成本高昂。虽然神经算子（NOs）为求解偏微分方程提供了一种高效的替代方案，但它们面临两个独特于多相系统的核心挑战：由相界面空间异质性引起的频谱偏差问题和昂贵的高质量场数据持续稀缺的问题。本文引入了接口信息感知神经算子（IANO），这是一种新型架构，通过利用易于获取的接口数据（如拓扑结构和位置）来缓解这些问题。接口数据本质上包含了物理场数据中缺失的高频特征，并有助于解决频谱偏差问题。IANO集成了一个接口感知的功能编码机制以捕捉动态耦合关系，并采用了一种几何感知的位置编码方法，以增强空间精度并实现点对点超分辨率提升。 在多个多相流案例中的实证结果表明，与现有的NO基准模型相比，IANO实现了显著的准确性改进（最高可达约10%）。此外，IANO在低数据量和噪声环境下的泛化能力更强，证实了其在基于AI的实际且高效的数据驱动多相流模拟中的实用性。
URL
https://arxiv.org/abs/2511.08625
