用傅里叶神经算子训练自监督布料模拟器，跨分辨率稳定准确。
FNOPT: Resolution-Agnostic, Self-Supervised Cloth Simulation using Meta-Optimization with Fourier Neural Operators
- 将时间积分建模为优化问题，用FNO参数化神经优化器。
- 仅在粗网格上训练，可泛化到细网格，捕捉褶皱并保持稳定。
- 无需标注数据，适合需要高精度与跨分辨率鲁棒性的应用。
我们提出FNOpt，一种自监督布料模拟框架，将时间积分形式化为优化问题，并训练一个由傅里叶神经算子（FNO）参数化的分辨率无关神经优化器。以往神经模拟器常依赖大量真实数据或牺牲细节，且在不同分辨率和运动模式间泛化能力差。相比之下，FNOpt在不重新训练的情况下，能模拟出物理上合理的布料动态，在多种网格分辨率和运动模式下实现稳定且精确的轨迹预测。仅在粗网格上使用基于物理的损失函数训练，即可推广至更细网格，有效捕捉细尺度褶皱并保持轨迹稳定性。在基准布料模拟数据集上的广泛评估表明，FNOpt在分布外设置下，相比已有学习型方法在准确性和鲁棒性方面均表现更优。这些结果表明，基于FNO的元优化是布料模拟中极具前景的替代方案，减少了对标注数据的需求，并提升了跨分辨率可靠性。
原文摘要 · Abstract (English)
We present FNOpt, a self-supervised cloth simulation framework that formulates time integration as an optimization problem and trains a resolution-agnostic neural optimizer parameterized by a Fourier neural operator (FNO). Prior neural simulators often rely on extensive ground truth data or sacrifice fine-scale detail, and generalize poorly across resolutions and motion patterns. In contrast, FNOpt learns to simulate physically plausible cloth dynamics and achieves stable and accurate rollouts across diverse mesh resolutions and motion patterns without retraining. Trained only on a coarse grid with physics-based losses, FNOpt generalizes to finer resolutions, capturing fine-scale wrinkles and preserving rollout stability. Extensive evaluations on a benchmark cloth simulation dataset demonstrate that FNOpt outperforms prior learning-based approaches in out-of-distribution settings in both accuracy and robustness. These results position FNO-based meta-optimization as a compelling alternative to previous neural simulators for cloth, thus reducing the need for curated data and improving cross-resolution reliability.
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