标题： 从稀疏的体外测量中进行流体-结构相互作用的神经推断 显示英文标题

标题： Neural inference of fluid-structure interactions from sparse off-body measurements

摘要： 我们报告了一种新颖的物理信息神经框架，用于从稀疏的单相流动观测中重建非定常流体-结构相互作用（FSI）。 我们的方法将模态表面模型与流体和固体动力学的坐标神经表示相结合，受流体的控制方程和界面条件的约束。 仅使用体外拉格朗日粒子轨迹和移动壁面边界条件，该方法可以推断出流动场和结构运动。 它不需要固体的本构模型或表面位置的测量，尽管包括这些可以提高性能。 通过两个典型的FSI基准测试展示了重建结果：二维摆动板的涡激振动和三维柔性管道中的脉冲波传播。 在两种情况下，即使在移动界面附近数据稀疏，该框架都能准确重建流动状态和结构变形。 一个关键结果是，即使包含超出解析结构所需的额外变形模式，重建结果仍然具有鲁棒性，从而消除了对截断正则化的需要。 这代表了物理信息神经网络在从单相观测中学习耦合多相动力学的新应用。 该方法使得在流动测量稀疏且结构测量异步或完全不可用的实验中能够进行定量FSI分析。 显示英文摘要

摘要： We report a novel physics-informed neural framework for reconstructing unsteady fluid-structure interactions (FSI) from sparse, single-phase observations of the flow. Our approach combines modal surface models with coordinate neural representations of the fluid and solid dynamics, constrained by the fluid's governing equations and an interface condition. Using only off-body Lagrangian particle tracks and a moving-wall boundary condition, the method infers both flow fields and structural motion. It does not require a constitutive model for the solid nor measurements of the surface position, although including these can improve performance. Reconstructions are demonstrated using two canonical FSI benchmarks: vortex-induced oscillations of a 2D flapping plate and pulse-wave propagation in a 3D flexible pipe. In both cases, the framework achieves accurate reconstructions of flow states and structure deformations despite data sparsity near the moving interface. A key result is that the reconstructions remain robust even as additional deformation modes are included beyond those needed to resolve the structure, eliminating the need for truncation-based regularization. This represents a novel application of physics-informed neural networks for learning coupled multiphase dynamics from single-phase observations. The method enables quantitative FSI analysis in experiments where flow measurements are sparse and structure measurements are asynchronous or altogether unavailable.