用置信预测量化动态系统模型不确定性，理论可靠且适合时序任务。
Conformal Prediction on Quantifying Uncertainty of Dynamic Systems
- 引入置信预测评估物理系统动态模型的不确定性
- 在偏微分方程数据集上验证了方法的有效性，支持直接滚动预测
- 适用于需要可靠不确定估计的时序建模场景
众多研究致力于从视频数据中学习和理解物理系统的动态特性，如空间智能。人工智能需要对模型不确定性进行定量评估以确保可靠性。然而，目前仍缺乏对不确定性（尤其是物理数据不确定性）的系统性评估。本文提出将置信预测引入动态系统不确定性评估，提供理论保障的方法。通过基准算子学习方法验证该方法，同时在偏微分方程数据集上对比了蒙特卡洛丢弃与集成方法，实现通过直接滚动预测有效评估不确定性，特别适用于时间序列任务。
原文摘要 · Abstract (English)
Numerous studies have focused on learning and understanding the dynamics of physical systems from video data, such as spatial intelligence. Artificial intelligence requires quantitative assessments of the uncertainty of the model to ensure reliability. However, there is still a relative lack of systematic assessment of the uncertainties, particularly the uncertainties of the physical data. Our motivation is to introduce conformal prediction into the uncertainty assessment of dynamical systems, providing a method supported by theoretical guarantees. This paper uses the conformal prediction method to assess uncertainties with benchmark operator learning methods. We have also compared the Monte Carlo Dropout and Ensemble methods in the partial differential equations dataset, effectively evaluating uncertainty through straight roll-outs, making it ideal for time-series tasks.
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