神经潜在任意拉格朗日-欧拉网格用于流固耦合
Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction
AI总结：
Fisale通过多尺度潜在ALE网格和分区耦合模块，实现复杂双向流固耦合问题的高效建模与学习。
摘要：
流固耦合（FSI）问题在许多科学和工程应用中都是基础性的，但有效捕捉高度非线性的双向相互作用仍然是一个重大挑战。现有的深度学习方法大多局限于简化的一次性FSI场景，通常假设刚性且静态的固体以降低复杂性。即使在双向设置中，现有方法也难以捕捉动态、异质的相互作用，因为缺乏跨域意识。在本文中，我们介绍了Fisale，一种用于处理复杂双向FSI问题的数据驱动框架。它受到经典数值方法的启发，即任意拉格朗日-欧拉（ALE）方法和分区耦合算法。Fisale明确将耦合界面作为独立组件进行建模，并利用多尺度潜在ALE网格，为不同领域提供统一的、几何感知的嵌入。分区耦合模块（PCM）进一步将问题分解为结构化的子步骤，使非线性依赖关系的逐步建模成为可能。与现有模型相比，Fisale引入了更灵活的框架，能够在统一的表示上迭代处理固体、流体及其耦合界面的复杂动态，并能够扩展学习复杂的双向FSI行为。实验上，Fisale在三个现实相关的挑战性FSI场景中表现出色，涵盖2D、3D以及各种任务。代码可在https://github.com/therontau0054/Fisale上获得。
英文摘要：
Fluid-solid interaction (FSI) problems are fundamental in many scientific and engineering applications, yet effectively capturing the highly nonlinear two-way interactions remains a significant challenge. Most existing deep learning methods are limited to simplified one-way FSI scenarios, often assuming rigid and static solid to reduce complexity. Even in two-way setups, prevailing approaches struggle to capture dynamic, heterogeneous interactions due to the lack of cross-domain awareness. In this paper, we introduce \textbf{Fisale}, a data-driven framework for handling complex two-way \textbf{FSI} problems. It is inspired by classical numerical methods, namely the Arbitrary Lagrangian-Eulerian (\textbf{ALE}) method and the partitioned coupling algorithm. Fisale explicitly models the coupling interface as a distinct component and leverages multiscale latent ALE grids to provide unified, geometry-aware embeddings across domains. A partitioned coupling module (PCM) further decomposes the problem into structured substeps, enabling progressive modeling of nonlinear interdependencies. Compared to existing models, Fisale introduces a more flexible framework that iteratively handles complex dynamics of solid, fluid and their coupling interface on a unified representation, and enables scalable learning of complex two-way FSI behaviors. Experimentally, Fisale excels in three reality-related challenging FSI scenarios, covering 2D, 3D and various tasks. The code is available at \href{https://github.com/therontau0054/Fisale}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
