提出新型神经网络架构，提升非结构网格流体模拟的精度与效率。
Transported Memory Networks accelerating Computational Fluid Dynamics
- 基于传输记忆机制，适配非结构化网格的流体求解器
- 在点级和统计层面优于或媲美现有方法的精度与速度
- 适合工业级复杂网格流体模拟，无需依赖特定网格结构
近年来，将神经网络与可微分偏微分方程求解器结合在流体模拟中展现出良好前景。然而，多数方法依赖卷积神经网络及在笛卡尔网格上运行的定制求解器，具备对单元数据的高效访问。这一选择在工业级求解器所用的非结构化网格上面临挑战，因仅能访问邻近单元。本文提出一种新架构——传输记忆网络（Transported Memory Networks），其灵感源自传统湍流模型与循环神经网络，完全兼容任意离散化方式。结果表明，该方法在点级和统计层面均达到或超越先前方法的精度与计算效率。
原文摘要 · Abstract (English)
In recent years, augmentation of differentiable PDE solvers with neural networks has shown promising results, particularly in fluid simulations. However, most approaches rely on convolutional neural networks and custom solvers operating on Cartesian grids with efficient access to cell data. This particular choice poses challenges for industrial-grade solvers that operate on unstructured meshes, where access is restricted to neighboring cells only. In this work, we address this limitation using a novel architecture, named Transported Memory Networks. The architecture draws inspiration from both traditional turbulence models and recurrent neural networks, and it is fully compatible with generic discretizations. Our results show that it is point-wise and statistically comparable to, or improves upon, previous methods in terms of both accuracy and computational efficiency.
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