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

• URL: http://arxiv.org/abs/2511.08625v1
• Date: Thu, 13 Nov 2025 01:00:56 GMT
• Title: Cross-Field Interface-Aware Neural Operators for Multiphase Flow Simulation
• Authors: ZhenZhong Wang, Xin Zhang, Jun Liao, Min Jiang,
• Abstract summary: Interface Information-Aware Neural Operator (IANO) is a novel framework that explicitly leverages interface information as a physical prior to enhance the prediction accuracy.<n>IANO outperforms baselines by $sim$10% in accuracy for multiphase flow simulations while maintaining robustness under data-scarce and noise-perturbed conditions.
• Score: 10.347532590928685
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction [20.862974213192988]
  We introduce textbfFisale, a data-driven framework for handling complex two-way textbfFSI problems.<n>Compared to existing models, Fisale introduces a more flexible framework that iteratively handles complex dynamics of solid, fluid and their coupling interface.<n>Fisale excels in three reality-related challenging FSI scenarios, covering 2D, 3D and various tasks.
  arXiv  Detail & Related papers  (2026-02-28T19:23:29Z)
• Data-Driven Deep MIMO Detection:Network Architectures and Generalization Analysis [50.20709408241935]
  This paper proposes inspecting the fully data-driven DeepSIC detection within a Network-of-MLPs architecture.<n>Within such an architecture, DeepSIC can be upgraded as a graph-based message-passing process using Graph Neural Networks (GNNs)<n>GNNSIC achieves excellent expressivity comparable to DeepSIC with substantially fewer trainable parameters.
  arXiv  Detail & Related papers  (2026-02-13T04:38:51Z)
• FAIM: Frequency-Aware Interactive Mamba for Time Series Classification [87.84511960413715]
  Time series classification (TSC) is crucial in numerous real-world applications, such as environmental monitoring, medical diagnosis, and posture recognition.<n>We propose FAIM, a lightweight Frequency-Aware Interactive Mamba model.<n>We show that FAIM consistently outperforms existing state-of-the-art (SOTA) methods, achieving a superior trade-off between accuracy and efficiency.
  arXiv  Detail & Related papers  (2025-11-26T08:36:33Z)
• Convolutional autoencoders for the reconstruction of three-dimensional interfacial multiphase flows [0.0]
  We focus on the accuracy of reconstructing multiphase flow volume/mass fractions with a standard convolutional architecture.<n>This study clarifies the best practices for reducing the dimensionality of multiphase flows via autoencoders.
  arXiv  Detail & Related papers  (2025-08-06T05:01:13Z)
• Physics-Informed Neural Networks for Enhanced Interface Preservation in Lattice Boltzmann Multiphase Simulations [4.744635045603924]
  This paper presents an improved approach for preserving sharp interfaces in multiphase Lattice Boltzmann Method (LBM) simulations using Physics-Informed Neural Networks (PINNs)<n> Interface diffusion is a common challenge in multiphase LBM, leading to reduced accuracy in simulating phenomena where interfacial dynamics are critical.<n>We propose a coupled PINN-LBM framework that maintains interface sharpness while preserving the physical accuracy of the simulation.
  arXiv  Detail & Related papers  (2025-04-13T08:29:00Z)
• COMPOL: A Unified Neural Operator Framework for Scalable Multi-Physics Simulations [21.983719504206878]
  COMPOL is a novel coupled multiphysics operator learning framework.<n>It incorporates sophisticated recurrent and attentionbased aggregation mechanisms effectively modeling interdependencies among interacting physical processes within latent feature spaces.<n>It consistently achieves superior predictive accuracy compared to stateoftheart methods.
  arXiv  Detail & Related papers  (2025-01-28T20:58:55Z)
• Neuromorphic Wireless Split Computing with Multi-Level Spikes [69.73249913506042]
  Neuromorphic computing uses spiking neural networks (SNNs) to perform inference tasks.<n> embedding a small payload within each spike exchanged between spiking neurons can enhance inference accuracy without increasing energy consumption.<n> split computing - where an SNN is partitioned across two devices - is a promising solution.<n>This paper presents the first comprehensive study of a neuromorphic wireless split computing architecture that employs multi-level SNNs.
  arXiv  Detail & Related papers  (2024-11-07T14:08:35Z)
• Multi-fidelity physics constrained neural networks for dynamical systems [16.6396704642848]
  We propose the Multi-Scale Physics-Constrained Neural Network (MSPCNN) MSPCNN offers a novel methodology for incorporating data with different levels of fidelity into a unified latent space. Unlike conventional methods, MSPCNN also manages to employ multi-fidelity data to train the predictive model.
  arXiv  Detail & Related papers  (2024-02-03T05:05:26Z)
• Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing [54.168567276280505]
  We propose a novel Mutual Information-driven Triple interaction Network (MITNet) for image dehazing. The first stage, named amplitude-guided haze removal, aims to recover the amplitude spectrum of the hazy images for haze removal. The second stage, named phase-guided structure refined, devotes to learning the transformation and refinement of the phase spectrum.
  arXiv  Detail & Related papers  (2023-08-14T08:23:58Z)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.