{"ID":2844230,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.08625","arxiv_id":"2511.08625","title":"Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation","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.","short_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...","url_abs":"https://arxiv.org/abs/2511.08625","url_pdf":"https://arxiv.org/pdf/2511.08625v2","authors":"[\"Zhenzhong Wang\",\"Xin Zhang\",\"Jun Liao\",\"Min Jiang\"]","published":"2025-11-09T01:13:40Z","proceeding":"physics.flu-dyn","tasks":"[\"physics.flu-dyn\",\"cs.AI\",\"cs.LG\"]","methods":"[]","has_code":false}