Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction

TL;DR

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.

Abstract

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}.

Figures (13)

• Figure 1: Fluid-solid interaction scenarios. (a.1) and (a.2) depict flow-around-body scenarios; (b.1) and (b.2) focus on aerodynamic analysis of wings; (c) illustrates the periodic dynamics of a venous valve; (a.1) and (b.1) represent one-way FSI cases, while the others involve two-way FSI.
• Figure 2: The overview of Fisale. (a) describes the overall structure of Fisale; (b) depicts the pipeline of the processor; (c) shows the pipeline of the latent ALE grid initialization.
• Figure 3: Local visualization of prediction results on solid displacement and fluid $x$-velocity. Red circle indicates the domain with most solid displacement and sharp fluid velocity change.
• Figure 4: Visualization of ground truth and prediction results. The red circle indicates the distortion of solid shape, where Fisale can effectively handle.
• Figure 5: The physical domain of venous valve simulation model.