研究人员开发了一种新的基于雅可比矩阵的谱审计方法，用于评估神经算子和上下文算子学习模型。该方法超越了简单的预测误差，评估了局部动力学结构，包括灵敏度、频率响应和稳定性。该审计可以揭示标准指标可能遗漏的算子保真度问题，例如高频退化或提示-算子不一致性，为学习到的算子提供更全面的诊断。 AI
影响 为神经算子提供了一个更鲁棒的评估框架，有望在科学领域带来更可靠、更稳定的AI模型。
排序理由 该集群描述了一篇介绍新研究方法的学术论文。
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AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
arXiv:2606.02427v1 Announce Type: cross Abstract: Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not guarantee the correct local dynamical structure. A model may match solutions whi…
Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not guarantee the correct local dynamical structure. A model may match solutions while exhibiting incorrect sensitivities, distorted f…
Existing evaluations of neural operators and in-context operator learning rely primarily on prediction error, but accurate output prediction does not guarantee the correct local dynamical structure. A model may match solutions while exhibiting incorrect sensitivities, distorted f…