用可信证据模型量化模拟预测的不确定性，提升可靠性。
ConfEviSurrogate: A Conformalized Evidential Surrogate Model for Uncertainty Quantification
- 融合可信证据与置信校准，分离多种不确定性来源。
- 在宇宙学、海洋和流体模拟中实现高精度预测与可靠区间。
- 适合需要可解释不确定性的科学仿真研究者使用。
代理模型在科学领域广泛用于近似复杂仿真数据，但其固有不确定性（如模拟噪声和预测误差）常被忽视，导致预测不可靠。现有方法如蒙特卡洛丢弃和集成模型成本高、难以区分不确定性类型，且预测区间缺乏保证覆盖。为此，我们提出ConfEviSurrogate——一种基于置信校准的可信证据代理模型，能高效学习高阶证据分布，直接预测仿真结果，分离不确定性来源，并提供具有保证覆盖的预测区间。通过置信预测校准步骤，进一步提升区间的可靠性与效率。该模型在宇宙学、海洋动力学和流体动力学等多种仿真任务中均表现出精准预测与稳健的不确定性估计。
原文摘要 · Abstract (English)
Surrogate models, crucial for approximating complex simulation data across sciences, inherently carry uncertainties that range from simulation noise to model prediction errors. Without rigorous uncertainty quantification, predictions become unreliable and hence hinder analysis. While methods like Monte Carlo dropout and ensemble models exist, they are often costly, fail to isolate uncertainty types, and lack guaranteed coverage in prediction intervals. To address this, we introduce ConfEviSurrogate, a novel Conformalized Evidential Surrogate Model that can efficiently learn high-order evidential distributions, directly predict simulation outcomes, separate uncertainty sources, and provide prediction intervals. A conformal prediction-based calibration step further enhances interval reliability to ensure coverage and improve efficiency. Our ConfEviSurrogate demonstrates accurate predictions and robust uncertainty estimates in diverse simulations, including cosmology, ocean dynamics, and fluid dynamics.
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