用神经网络集成提升海洋模型参数敏感性分析的可靠性
Ensembles of Neural Surrogates for Parametric Sensitivity in Ocean Modeling
- 构建神经网络集成模型，同时优化预测与敏感性计算
- 实现对海洋模型参数变化的高精度梯度估计
- 适合气候模拟与模型调参研究者使用
准确模拟海洋对理解地球系统至关重要。尽管低分辨率模拟效率高，但仍需依赖大量不确定的参数化方案来处理未解析过程。然而，模型对这些参数化的敏感性难以量化，导致参数调整困难。深度学习代理模型在高效计算参数敏感性（即偏导数）方面展现出潜力，但缺乏真实导数作为评估基准，其可靠性难判断。本文通过大规模超参数搜索与集成学习，提升了前向预测、自回归滚动预测以及反向伴随敏感性估计的性能。特别是，集成方法提供了函数值及其导数的认知不确定性，显著增强了神经代理模型在决策中的可信度。
原文摘要 · Abstract (English)
Accurate simulations of the oceans are crucial in understanding the Earth system. Despite their efficiency, simulations at lower resolutions must rely on various uncertain parameterizations to account for unresolved processes. However, model sensitivity to parameterizations is difficult to quantify, making it challenging to tune these parameterizations to reproduce observations. Deep learning surrogates have shown promise for efficient computation of the parametric sensitivities in the form of partial derivatives, but their reliability is difficult to evaluate without ground truth derivatives. In this work, we leverage large-scale hyperparameter search and ensemble learning to improve both forward predictions, autoregressive rollout, and backward adjoint sensitivity estimation. Particularly, the ensemble method provides epistemic uncertainty of function value predictions and their derivatives, providing improved reliability of the neural surrogates in decision making.
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