外观
论文arXiv 2610.1434219 天前
使用 Physics-Informed Neural ODEs 的连续时间布料动力学
原文链接：Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs（arXiv 2610.14342 · 发布日期：2026-10-09）
Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs
ContinuousCloth用物理约束神经ODE求解连续时间布料动力学，并在仿真和真实序列验证插值、参数估计与加速效果。
核心要点
- 经典时间步进仿真器只在离散积分网格返回轨迹，限制任意时间查询。
- 方法在神经轨迹模型中约束弹簧-质点ODE，并用自动微分求导。
- 纯物理设置下，六种边界条件中精度接近高精度求解器，最高快17.4倍。
- 融合稀疏噪声观测后，插值误差降7.3倍和2.4倍，高噪声参数误差低2.6倍。
验证范围与局限：验证包括仿真六种边界条件、仿真插值与参数估计，以及真实布料序列插值。
英文摘要 (Abstract)
We present ContinuousCloth, a continuous-time, physics-informed neural solver for deformable cloth dynamics that enforces a spring--mass ODE within a neural trajectory model. Unlike classical time-stepping simulators that return trajectories only on a discrete integration grid, ContinuousCloth supports querying at arbitrary timestamps and computing displacement, velocity, and acceleration via automatic differentiation. We evaluate ContinuousCloth along two dimensions. In a physics-only setting, ContinuousCloth achieves trajectory accuracy comparable to high-precision solvers across six diverse boundary-condition scenarios while reducing runtime by up to 17.4 times. When fused with sparse and noisy observations, the spring--mass ODE acts as a strong inductive bias for downstream tasks: interpolation and parameter estimation. That is, ContinuousCloth reduces interpolation error by 7.3 times over a data-only neural baseline in simulation and 2.4 times on real-world cloth sequences, and achieves 2.6 times lower parameter error than a differentiable-solver baseline under high-noise conditions, demonstrating consistent robustness under realistic measurement imperfections.
作者：Yingdong Ru, Lipeng Zhuang, Zhuo He, Gerardo Aragon Camarasa
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