Steering Diffusion Models to Rare Events with Sequential Monte Carlo
Diffusion models are increasingly used as surrogates for expensive simulators in weather prediction, molecular dynamics, and materials design. In these models, computing the probability $p_0[E]$ of an event $E$ is difficult, especially when the event of interest is rare. A stable estimate using Monte Carlo becomes computationally intractable, requiring a growing sample size $\propto\!1/p_0[E]$ to compensate for an increasing rarity. In this paper, we present Diffusion rare event sampling with Sequential Monte Carlo (DireSMC), a sequential Monte Carlo scheme that guides a population of weighted samples towards the rare event, giving access not only to samples but also to a calibrated estim...
| Authors | Aavash Subedi, Tim Reichelt, Christopher Williams et al. |
|---|---|
| Primary category | stat.ML |
| Published | 6 October 2026 |
| Last updated at source | 13 October 2026 |
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