Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs

Key points

• 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.1
• We evaluate ContinuousCloth along two dimensions.1
• When fused with sparse and noisy observations, the spring--mass ODE acts as a strong inductive bias for downstream tasks: interpolation and parameter estimation.1
• 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.1

Sources (1)

• [1]Continuous-Time Cloth Dynamics with Physics-Informed Neural ODEs
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 09:35 PM

  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.

Extractive summary: sentences quoted from the sources.