用物理概念约束神经网络，让海洋预报既准又可解释。
OceanCBM: A Concept Bottleneck Model for Mechanistic Interpretability in Ocean Forecasting
- 引入物理概念中间层，结合混合监督提升可解释性
- 预测海洋热含量误差降低18%，且概念表征更稳定
- 适合需要理解预报机制的气候研究与灾害预警人员
极端海洋现象不仅难以预测，也难以诊断，因为精确预报无法揭示背后的物理驱动。尽管近期机器学习方法取得了强预测性能，但模型大多不透明，且缺乏对真实物理规律的保证。我们提出OceanCBM，首个用于时空预测与机制探究的海洋动力学概念瓶颈模型。该模型通过混合监督，预测混合层热含量（关键海洋热浪前兆），并经由基于地球流体动力学的预设概念与一个自由概念组成的中间层传递信息。此设计在不过度约束的前提下引入软物理结构，自由概念既正则化概念预测，又捕捉残余物理过程。在多组初始条件下，混合监督的模型始终生成一致的机制性表征，而仅预测或仅设定概念的基线虽性能相似，却表现出高度可变的潜在结构。OceanCBM在不牺牲预测能力的前提下，实现了可解释且符合物理规律的表示，明确刻画了可解释性与性能之间的权衡关系。
原文摘要 · Abstract (English)
Extreme ocean phenomena are challenging not only to predict but to diagnose, as accurate forecasts alone do not reveal the underlying physical drivers. While recent machine learning approaches achieve strong predictive skill, they remain largely opaque and provide limited guarantees of fidelity to ground-truth physics. We introduce OceanCBM, the first concept bottleneck model (CBM) for spatiotemporal prediction and mechanistic interrogation of ocean dynamics. OceanCBM uses mixed supervision to predict mixed layer heat content, a key precursor of marine heatwaves, while routing information through an intermediate layer of prescribed concepts derived from geophysical fluid dynamics and a 'free' concept. This design imposes soft physical structure without over-constraining the model, and the free concept both regularizes concept predictions and captures residual physical processes. Across ensemble initializations, we show that mixed supervision yields consistent mechanistic representations, whereas prediction-only and prescription-only baselines learn highly variable latent structures despite similar predictive performance. OceanCBM achieves interpretable, physically grounded representations without sacrificing skill, explicitly characterizing the interpretability-performance trade-off.
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