用深度学习加速气候突变模拟，预测海流崩溃更准更快
Deep Learning Surrogates for Emulating Stochastic Climate Tipping Dynamics
- 基于时序融合变压器，融合多变量时间序列与静态参数建模
- 预测大西洋/太平洋海流崩溃时间，误差极小且保留随机不确定性
- 比传统模拟快465倍，适合需要快速迭代的气候研究
本文探索一种融入动力学信息的时序融合变压器（TFT），作为计算昂贵的地球系统模拟的数据驱动代理模型。聚焦描述全球海洋输送的多变量时间序列，展示该代理模型在数千个时间步上预测突变事件的能力。数据包含多达21条非平稳时间序列，以及描述自由参数和初始条件的静态协变量。通过修改架构与目标函数，所构建的代理模型能高精度预判大西洋与太平洋海流崩溃的时间，并捕捉集成预测中过渡时间的随机不确定性。该学习型代理模型相较数值模拟实现465倍的计算加速，同时保持对参数与初始条件的可微性。
原文摘要 · Abstract (English)
This work explores a dynamics-informed Temporal Fusion Transformer (TFT) as a data-driven surrogate for computationally intensive Earth system simulations. Focusing on multivariate time series describing global ocean transport, we demonstrate the surrogate's ability to forecast tip events across thousands of time steps. The data involve up to 21 non-stationary time series in addition to static covariates describing free parameters and initial conditions. Modifications to the architecture and objective function yield a surrogate that anticipates the timing of Atlantic and Pacific collapses to high fidelity and captures the stochastic uncertainty in transition timing across ensemble predictions. The learned surrogate achieves a 465x computational speedup over the numerical simulator while maintaining differentiability with respect to parameters and initial conditions.
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