用不确定性自适应调整步长，提升长时序物理系统预测精度
DiffusionRollout: Uncertainty-Aware Rollout Planning in Long-Horizon PDE Solving
- 根据采样标准差动态调整推理步长，减少误差累积
- 在长轨迹预测中误差降低，且与真实轨迹相关性更高
- 适合需要高可靠性的科学计算与物理模拟场景
我们提出DiffusionRollout，一种针对自回归扩散模型的新型选择性滚动规划策略，旨在缓解由偏微分方程（PDE） governing 物理系统长期预测中的误差累积问题。基于近期验证的概率化PDE求解方法，我们进一步探索其量化预测不确定性的能力，并证实预测误差与多样本计算的标准差存在强相关性——支持将其作为模型预测置信度的代理指标。基于此观察，我们引入一种在自回归滚动过程中自适应选择步长的机制，通过减少对不准确前序输出的依赖，提升长期预测可靠性。在多个长轨迹PDE预测基准上的广泛评估验证了所提不确定性度量和自适应规划策略的有效性：预测误差更低，且能维持更长的高相关性预测轨迹。
原文摘要 · Abstract (English)
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 high correlation with their ground truths.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。