AI 中文总结
OmniRemesh是解决大变形CPFEM模拟网格畸变及反演分析拓扑问题的统一框架，可实现80%拉伸变形下的稳定模拟与本构参数的准确反校。
AI 中文摘要
大变形晶体塑性有限元法（CPFEM）模拟常受累积的网格畸变限制，这会降低模拟精度与数值稳定性，而自适应重网格化会引入离散拓扑变化，阻碍基于梯度的反演分析。本文提出OmniRemesh这一统一框架，通过两项进展解决正问题与反问题挑战：其一，结构驱动的重网格化方法可根据微观结构几何与演化的力学状态动态重新分配局部网格分辨率，通过细化晶界与局部变形区域、在其余区域保留较粗网格，该方法维持了网格质量与物理一致性，提升了大变形计算的精度与鲁棒性，且无需采用均匀密集离散即可解决晶粒尺度的异质性问题；其二，冻结重网格化分支策略在参数信赖域内局部固定网格序列，并随参数演化定期更新，该处理在拓扑变化下仍能提供近似自动微分灵敏度，可针对宏观与局部观测量高效完成本构参数的反校。数值算例表明，该方法可实现拉伸变形达80%时的准确稳定CPFEM模拟，反校成功恢复了宏观与局部响应，OmniRemesh为大变形CPFEM及感知重网格化的本构校准提供了实用框架。
英文摘要
Large-deformation crystal plasticity finite element method (CPFEM) simulations are often limited by accumulated mesh distortion, which degrades accuracy and numerical stability, while adaptive remeshing introduces discrete topology changes that impede gradient-based inverse analysis. We present OmniRemesh, a unified framework that addresses these forward and inverse challenges through two developments. First, a structure-driven remeshing method dynamically redistributes local mesh resolution according to both microstructural geometry and the evolving mechanical state. By refining grain boundaries and localized deformation regions while retaining a coarser mesh elsewhere, the method maintains mesh quality and physical consistency, improves the accuracy and robustness of large-deformation calculations, and resolves grain-scale heterogeneity without uniformly dense discretization. Second, a frozen-remeshing-branch strategy locally fixes the mesh sequence within a parameter trust region and periodically updates it as the parameters evolve. This treatment provides approximate automatic-differentiation sensitivities despite topology changes, enabling efficient inverse calibration of constitutive parameters against both macroscopic and local observables. Numerical examples demonstrate accurate and stable CPFEM simulations up to 80\% tensile deformation. The inverse calibration successfully recovers both macroscopic and local responses. OmniRemesh thus provides a practical framework for large-deformation CPFEM and remeshing-aware constitutive calibration.
