计算机应用研究2026，Vol.43Issue(5)：1372-1377,6.DOI:10.19734/j.issn.1001-3695.2025.09.0382
基于物理约束注意力增强的拉格朗日流体仿真
Physics-constrained attention enhanced Lagrangian fluid simulation
摘要
Abstract
To address the fundamental conflict between data-driven methods and physical principles in neural network fluid simulation where excessive reliance on learning leads to deviations from conservation laws,while strong constraints impair com-plex flow representation,particularly causing structural distortion in long-term simulations involving boundary interactions,this paper developed a novel MFANet-PD.The model employed a dual-channel cooperative architecture:the FFE dynamically extracted fine-grained motion features using continuous convolution operators and channel attention recalibration;a physics-aware constraint path explicitly handled obstacle boundaries and projects conservation quantities,achieving synergistic optimiza-tion of data-driven and physical principles.Experiments on the Liquid3D dataset demonstrate that,compared to baseline models,MFANet-PD improved short-term prediction accuracy by 14.6％,reduced long-term instability by 18.7％,and enhanced distribution consistency by 11.1％,while exhibiting superior resistance to structural distortion.Ablation studies confirmed a 10.1％reduction in long-sequence error for the full model versus the FFE-free version.Canyon terrain tests validated its ge-neralizability,effectively handling 60° inclined rock walls and dynamic scales from 2 000 to 8 000+particles.The algorithm significantly enhances computational accuracy and physical consistency in fluid simulation through multi-scale feature fusion and physics-constrained mechanisms.关键词
流体模拟/物理约束神经网络/多尺度流场注意力/连续卷积/粒子系统Key words
fluid simulation/physics-constrained neural networks/multi-scale flow-attentive/continuous convolution/parti-cle systems分类
信息技术与安全科学引用本文复制引用
童攀,朱雨馨,邹鑫,戈文一,魏敏..基于物理约束注意力增强的拉格朗日流体仿真[J].计算机应用研究,2026,43(5):1372-1377,6.基金项目
四川省科学技术厅重点研发项目(2024YFG0009) （2024YFG0009）
