发表机构
Beihang University; Morgan State University（北京航空航天大学; 摩根州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对准静态Biot系统强耦合与多尺度计算难题，提出HFS-TransNet混合方法，结合数据驱动与物理信息迭代，具备鲁棒性与可迁移性，并在脑水肿模拟中验证优越性能。
AI 中文摘要
准静态Biot系统控制着孔隙弹性介质中流体与固体的耦合相互作用，由于其强耦合和多尺度特性，给计算带来了重大挑战。为解决这些问题，我们提出了一种混合固定应力可迁移神经网络（HFS-TransNet）方法。在HFS-TransNet中，数据驱动的TransNet模块首先在初始化域上逼近观测数据，为后续物理信息阶段提供高质量的起点，随后该阶段通过TransNet驱动固定应力分裂迭代，并允许误差界。每次迭代后，验证域上的贪心更新策略防止模型退化，相对容差准则控制收敛终止。因此，HFS-TransNet继承了固定应力分裂方案的鲁棒性、数据驱动建模的灵活性以及TransNet的可迁移性，有效克服了Biot系统固有的强耦合和锁定不稳定性。消融研究以及与FS-FEM和FS-PINN的比较证实，HFS-TransNet在不同物理参数和边界条件下捕获多尺度耦合物理方面具有优越性能。通过桥接经典解耦迭代方案与混合科学机器学习，HFS-TransNet为复杂孔隙弹性模拟提供了新视角，其在脑水肿模拟中的成功应用证明了这一点。
英文摘要
The quasi-static Biot system, which governs coupled fluid-solid interactions in poroelastic media, poses significant computational challenges due to its strong coupling and multiscale nature. To address these, we propose a hybrid fixed-stress transferable neural network (HFS-TransNet) method. In HFS-TransNet, a data-driven TransNet module first approximates observed data on an initialization domain to supply a high-quality starting point for the subsequent physics-informed stage, which then drives fixed-stress splitting iterations via TransNet and admits an error bound. After each iteration, a greedy update strategy on a validation domain prevents model degradation and a relative tolerance criterion governs convergence termination. HFS-TransNet thereby inherits the robustness of the fixed-stress splitting scheme, the flexibility of data-driven modeling, and the transferability of TransNet, effectively overcoming the strong coupling and locking instability inherent in the Biot system. Ablation studies and comparisons with FS-FEM and FS-PINN confirm HFS-TransNet's superior performance in capturing multiscale coupled physics across varying physical parameters and boundary conditions. By bridging classical decoupled iterative schemes with hybrid scientific machine learning, HFS-TransNet offers a novel perspective for complex poroelastic simulations, as demonstrated by its successful application to brain edema simulations.
