图像回归模型的自适应共形预测及其在惯性约束聚变仿真器中的应用

中文摘要 AI 辅助

不确定性量化在科学机器学习中至关重要，其中黑盒、基于图像的模型越来越多地部署在高风险场景中。在许多此类应用中，模型输出为代价高昂的决策提供信息，然而大多数方法仅提供点估计，而不量化预测不确定性。这一挑战因模型内部的可访问性和可解释性有限而变得更加复杂，使得难以评估输入空间不同区域的可靠性。因此，迫切需要能够提供输入相关的不确定性估计的方法，以指导模型开发和下游实验。为满足这一需求，我们提出了使用最近邻的自适应共形预测（ACPNN），一种用于图像回归的输入自适应共形框架。ACPNN利用邻近样本的信息来产生局部自适应的不确定性估计，同时保持较低的计算成本。邻域结构通过使用具有自动相关性确定（ARD）核的高斯过程学习的缩放距离度量来定义。我们在一个用于模拟惯性约束聚变（ICF）模拟的扩散模型上展示了ACPNN的有效性，表明它实现了可靠且自适应的不确定性量化。

英文摘要

Uncertainty quantification is critical in scientific machine learning, where black-box, image-based models are increasingly deployed in high-stakes settings. In many such applications, model outputs inform costly decisions, yet most methods provide only point estimates without quantifying predictive uncertainty. This challenge is compounded by the limited accessibility and interpretability of model internals, making it difficult to assess reliability across different regions of the input space. As a result, there is a growing need for methods that can provide input-dependent uncertainty estimates to guide both model development and downstream experimentation. To address this need, we propose Adaptive Conformal Prediction using Nearest Neighbors (ACPNN), an input-adaptive conformal framework for image regression. ACPNN leverages information from neighboring samples to produce locally adaptive uncertainty estimates while maintaining low computational cost. The neighborhood structure is defined using a scaled distance metric learned via a Gaussian Process with an automatic relevance determination (ARD) kernel. We demonstrate the effectiveness of ACPNN on a diffusion model for emulating inertial confinement fusion (ICF) simulations, showing that it achieves reliable and adaptive uncertainty quantification.

发表机构

• Texas A&M University（德州农工大学）
• Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）

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