提出快速准确的神经算子解释方法，支持不规则网格数据。
OperatorSHAP: Fast and Accurate Shapley Value Estimation for Neural Operators
- 基于函数空间理论，设计适用于不规则网格的Shapley值估计方法
- 在不同网格分辨率下保持解释一致性，无需重新训练
- 适合物理仿真、气象预测等需安全决策的领域
理解模型预测对物理应用至关重要，其输出常用于结构载荷评估、天气预警和临床诊断等关键决策。Shapley值作为归因方法具有诸多理想属性，但推理时计算成本过高，限制了实际应用。现有加速解释方法如FastSHAP仅适用于同质输入，在物理应用中数据常来自不规则网格和几何结构时存在局限。本文提出OperatorSHAP，一种无网格依赖的归因方法及训练流程，可为神经算子训练类似FastSHAP的解释器。我们建立了函数空间归因的理论框架，与Aumann-Shapley值相联系。进一步证明，OperatorSHAP的解释在不同分辨率间保持一致性，且可在不同网格尺寸间迁移使用，无需重新训练。
原文摘要 · Abstract (English)
Understanding model predictions is essential for physical applications, where outputs often inform safety-critical decisions, such as structural load assessment, weather warnings, and clinical diagnosis. Shapley values satisfy many desirable properties as an attribution method, but their computational cost during inference hinders their practical use. Current amortized explainers, such as FastSHAP, are limited to homogeneous inputs, which is problematic for physical applications where data often comes from irregular grids and geometries. We introduce OperatorSHAP, a grid-agnostic attribution method and training procedure that allows us to train FastSHAP-like explainers for neural operators. We establish a theoretical framework for attributions in function space, connecting to Aumann-Shapley values. We further show that OperatorSHAP's explanations are consistent with state-of-the-art discrete Shapley values across resolutions and transfer across grid sizes without retraining.
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。