Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation
IANO's architecture: 1) The interface-aware multiple functions encoding mechanism jointly encodes both multiphysics fields and interface information to generate an interface-aware function embedding for each field. 2) The geometry-aware positional encoding mechanism produces positional embedding by explicitly linking multiphysics fields and interfaces to their positions. IANO integrates the function embeddings with the positional embeddings for multiphase flow prediction.
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.
Poster
BibTeX
To be updated