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.

翻译：本文提出FNOpt，一种自监督布料仿真框架，其将时间积分表述为优化问题，并训练一个由傅里叶神经算子参数化的分辨率无关神经优化器。现有神经仿真器通常依赖大量真实数据或牺牲细节精度，且在分辨率与运动模式间泛化能力较差。相比之下，FNOpt通过学习模拟物理可信的布料动力学，无需重新训练即可在不同网格分辨率与运动模式下实现稳定且精确的推演。仅基于粗网格与物理损失训练后，FNOpt能泛化至更精细分辨率，捕捉细微褶皱并保持推演稳定性。在标准布料仿真数据集上的广泛评估表明，FNOpt在分布外场景的精度与鲁棒性均优于现有基于学习的方法。这些结果确立了基于FNO的元优化作为布料神经仿真器的有力替代方案，从而减少对标注数据的依赖并提升跨分辨率可靠性。