为云微物理降阶模型提供无需修改训练的不确定性量化方法
Uncertainty Quantification for Reduced-Order Surrogate Models Applied to Cloud Microphysics
- 用置信预测法在隐空间中后验估计不确定性
- 准确给出云滴谱演化预测区间和全流程不确定性
- 适合需要可信模拟结果的气候与大气建模研究者
降阶模型（ROMs）能高效模拟高维物理系统，但缺乏可靠的不确定性量化方法。现有方法常依赖特定架构或训练方式，限制了灵活性与泛化能力。本文提出一种后验、模型无关的框架，用于隐空间降阶模型的预测不确定性量化，无需修改底层架构或训练过程。基于置信预测，该方法可对ROM流程中的隐空间动力学、重构结果及端到端预测分别估计统计预测区间。我们在云微物理的隐空间动力学模型上验证了该方法，结果显示其能准确预测云滴大小分布的演化，并全面量化整个模型流程的不确定性。
原文摘要 · Abstract (English)
Reduced-order models (ROMs) can efficiently simulate high-dimensional physical systems but lack robust uncertainty quantification methods. Existing approaches are frequently architecture- or training-specific, which limits flexibility and generalization. We introduce a post hoc, model-agnostic framework for predictive uncertainty quantification in latent space ROMs that requires no modification to the underlying architecture or training procedure. Using conformal prediction, our approach estimates statistical prediction intervals for multiple components of the ROM pipeline: latent dynamics, reconstruction, and end-to-end predictions. We demonstrate the method on a latent space dynamical model for cloud microphysics, where it accurately predicts the evolution of droplet-size distributions and quantifies uncertainty across the ROM pipeline.
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