Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation
This work addresses computational challenges in multiphase flow simulations for fields like engineering and physics, though it appears incremental as it builds on existing neural operator methods with specific enhancements.
The paper tackled the challenge of achieving high-resolution numerical accuracy in multiphase flow simulations, where neural operators often struggle due to spatial heterogeneity and data scarcity, and proposed the Interface Information-Aware Neural Operator (IANO) framework, which improved accuracy by approximately 10% compared to baselines while maintaining robustness in low-data and noisy conditions.
Multiphase flow systems, with their complex dynamics, field discontinuities, and interphase interactions, pose significant computational challenges for traditional numerical solvers. While neural operators offer efficient alternatives, they often struggle to achieve high-resolution numerical accuracy in these systems. This limitation primarily stems from the inherent spatial heterogeneity and the scarcity of high-quality training data in multiphase flows. In this work, we propose the Interface Information-Aware Neural Operator (IANO), a novel framework that explicitly leverages interface information as a physical prior to enhance the prediction accuracy. The IANO architecture introduces two key components: 1) An interface-aware multiple function encoding mechanism jointly models multiple physical fields and interfaces, thus capturing the high-frequency physical features at the interface. 2) A geometry-aware positional encoding mechanism further establishes the relationship between interface information, physical variables, and spatial positions, enabling it to achieve pointwise super-resolution prediction even in the low-data regimes. Experimental results demonstrate that IANO outperforms baselines by $\sim$10\% in accuracy for multiphase flow simulations while maintaining robustness under data-scarce and noise-perturbed conditions.