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[2602.13616] DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
Summary
The paper introduces DiffusionRollout, a strategy for improving long-horizon predictions in physical systems governed by PDEs by addressing error accumulation through adaptive rollout planning.
Why It Matters
This research is significant as it enhances the reliability of predictions in complex physical systems, which is crucial for various applications in engineering and science. By quantifying predictive uncertainty, it offers a method to improve decision-making in uncertain environments, potentially transforming how we approach long-term forecasting in AI.
Key Takeaways
- DiffusionRollout mitigates error accumulation in long-horizon predictions.
- The method quantifies predictive uncertainty, improving model confidence.
- Adaptive step size selection enhances prediction reliability.
- Extensive evaluations demonstrate lower prediction errors and longer trajectories.
- The approach is applicable to various physical systems governed by PDEs.
Computer Science > Artificial Intelligence arXiv:2602.13616 (cs) [Submitted on 14 Feb 2026] Title:DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving Authors:Seungwoo Yoo, Juil Koo, Daehyeon Choi, Minhyuk Sung View a PDF of the paper titled DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving, by Seungwoo Yoo and 3 other authors View PDF HTML (experimental) Abstract:We propose DiffusionRollout, a novel selective rollout planning strategy for autoregressive diffusion models, aimed at mitigating error accumulation in long-horizon predictions of physical systems governed by partial differential equations (PDEs). Building on the recently validated probabilistic approach to PDE solving, we further explore its ability to quantify predictive uncertainty and demonstrate a strong correlation between prediction errors and standard deviations computed over multiple samples-supporting their use as a proxy for the model's predictive confidence. Based on this observation, we introduce a mechanism that adaptively selects step sizes during autoregressive rollouts, improving long-term prediction reliability by reducing the compounding effect of conditioning on inaccurate prior outputs. Extensive evaluation on long-trajectory PDE prediction benchmarks validates the effectiveness of the proposed uncertainty measure and adaptive planning strategy, as evidenced by lower prediction errors and longer predicted trajectories that retain a hi...
