Stable partitioned techniques for simulating unsteady fluid-structure interaction (FSI) are known to be computationally expensive when high added-mass is involved. Multiple coupling strategies have been developed to accelerate these simulations, but often use predictors in the form of simple finite-difference extrapolations. In this work, we propose a non-intrusive data-driven predictor that couples reduced-order models of both the solid and fluid subproblems, providing an initial guess for the nonlinear problem of the next time step calculation. Each reduced order model is composed of a nonlinear encoder-regressor-decoder architecture and is equipped with an adaptive update strategy that adds robustness for extrapolation. In doing so, the proposed methodology leverages physics-based insights from high-fidelity solvers, thus establishing a physics-aware machine learning predictor. Using three strongly coupled FSI examples, this study demonstrates the improved convergence obtained with the new predictor and the overall computational speedup realized compared to classical approaches.

翻译：已知在涉及高附加质量时，用于模拟非定常流固耦合（FSI）的稳定分区技术计算成本高昂。多种耦合策略已被开发用于加速此类模拟，但通常采用简单的有限差分外推形式作为预测器。本文提出一种非侵入式数据驱动预测器，该预测器耦合了固体和流体子问题的降阶模型，为下一时间步计算的非线性问题提供初始猜想。每个降阶模型由非线性编码器-回归器-解码器架构组成，并配备自适应更新策略以增强外推的鲁棒性。通过此方式，该方法利用高保真求解器中的物理洞察，从而建立了一种物理感知的机器学习预测器。通过三个强耦合FSI实例，本研究展示了新预测器所带来的收敛性改进以及与经典方法相比实现的整体计算加速。