Data-Driven Reachability Analysis via Diffusion Models with PAC Guarantees
We present a data-driven framework for reachability analysis of nonlinear dynamical systems that requires no explicit model. A denoising diffusion probabilistic model learns the time-evolving state distribution of a dynamical system from trajectory data alone. The predicted reachable set takes the form of a sublevel set of a nonconformity score derived from the reconstruction error, with the threshold calibrated via the Learn Then Test procedure so that the probability of excluding a reachable state is bounded with high probability. Experiments on three nonlinear systems, a forced Duffing oscillator, a planar quadrotor, and a high-dimensional reaction-diffusion system, confirm that the em...
| Authors | Yanliang Huang, Peng Xie, Wenyuan Wu et al. |
|---|---|
| Primary category | eess.SY |
| Published | 31 March 2026 |
| Last updated at source | 31 March 2026 |
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