Neural Latent Arbitrary Lagrangian-Eulerian Grids for Fluid-Solid Interaction
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}.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
From Fixed Grids to Moving Particles:A Transferable Latent Operator for Fluid Dynamics
Lagrangian modeling is vital to fluid dynamics, as it characterizes particle transport and complements the Eulerian representation. However, Lagrangian trajectories are less commonly available than Eulerian fields, while…
DeepLag: Discovering Deep Lagrangian Dynamics for Intuitive Fluid Prediction
Accurately predicting the future fluid is vital to extensive areas such as meteorology, oceanology, and aerodynamics. However, since the fluid is usually observed from the Eulerian perspective, its moving and intricate d…
Future predictionPrediction3D Fluid Flow Estimation with Integrated Particle Reconstruction
The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. Almost all existing work processes the ac…
3D ReconstructionMotion EstimationAdvectiveNet: An Eulerian-Lagrangian Fluidic reservoir for Point Cloud Processing
This paper presents a novel physics-inspired deep learning approach for point cloud processing motivated by the natural flow phenomena in fluid mechanics. Our learning architecture jointly defines data in an Eulerian wor…
Point Cloud ClassificationVortexNet: Learning Complex Dynamic Systems with Physics-Embedded Networks
In this paper, we present a novel physics-rooted network structure that dramatically facilitates the learning of complex dynamic systems. Our method is inspired by the Vortex Method in fluid dynamics, whose key idea lies…