AI 中文总结
该研究针对带摩擦接触的布料模拟提出首个变分r自适应方法，通过退化激活的质量正则化解决失效问题，结合动态非线性求解器实现3-6倍加速，实验表明其在相同约束下视觉保真度优于固定网格。
AI 中文摘要
我们提出了现代布料管线中带摩擦接触的布料动力学与静力学模拟的首个r自适应方法。薄布料需要高有效空间分辨率来重现褶皱、折叠、屈曲及尖锐接触特征。然而，将现有变分r自适应应用于分段线性壳时，会出现两种耦合失效模式：离散增量势能（IP）优化可能陷入差的局部极小值，产生次优物理构型；还可能通过单元合并人为降低IP，使目标依赖的有限元近似失效。我们通过退化激活的质量正则化解决这两个问题：正则化器在形状良好的单元上保持非激活，保留各向异性自适应与局部加密，在退化附近则变得强有效，抑制虚假低能谷、改善从次优物理极小值的逃逸、防止单元聚集（一种布料特有的失效，即布料滑过尖锐接触特征时单元逐步合并）。为实现实用性能，我们引入动态非线性求解器，通过加速导数评估与针对r自适应迭代信赖域（ITR）求解的动态IPC容差更新，利用时间步内相干性，较现有最优ITR实现3-6倍加速。在具挑战性的摩擦接触场景实验表明，在相同顶点数与时间预算约束下，我们的方法比固定网格实现更高视觉保真度。
英文摘要
We present the first r-adaptive method for simulating cloth dynamics and statics with frictional contact in modern cloth pipelines. Thin cloth requires high effective spatial resolution to reproduce wrinkles, folds, buckling, and sharp contact features. However, applying existing variational r-adaptivity to piecewise-linear shells reveals two coupled failure modes. Discretized incremental-potential (IP) optimization can become trapped in poor local minima, yielding suboptimal physical configurations. It can also lower IP artificially by collapsing elements, invalidating the finite-element approximation on which the objective relies. We address both problems with degeneracy-activated quality regularization. The regularizer remains inactive for well-shaped elements, preserving anisotropic adaptation and local densification, but becomes strong near degeneracy. It suppresses spurious low-energy basins, improves escape from suboptimal physical minima, and prevents element bunching, a cloth-specific failure in which elements progressively collapse as cloth slides across sharp contact features. For practical performance, we introduce a dynamic nonlinear solver that exploits within-timestep coherence through accelerated derivative evaluation and dynamic IPC tolerance updates for r-adaptive iterative trust-region (ITR) solves. This yields a 3-6x speedup over prior optimal ITR. Experiments on challenging frictional-contact scenarios show that, under equal vertex-count and time-budget constraints, our method achieves higher visual fidelity than fixed meshes.
Comments11 pages
