Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation

TL;DR

This paper introduces IANO, a neural operator architecture that leverages interface data to improve multiphase flow simulations, addressing spectral bias and data scarcity issues for more accurate and efficient predictions.

Contribution

The paper proposes IANO, a novel neural operator that incorporates interface information and geometry-aware encoding to enhance multiphase flow modeling accuracy and generalization.

Findings

Achieves up to 10% accuracy improvement over existing methods.

Demonstrates superior generalization in low-data and noisy environments.

Effectively captures dynamic coupling and high-frequency interface features.

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.