We introduce the 'Stochastic Latent Transformer', a probabilistic deep learning approach for efficient reduced-order modelling of stochastic partial differential equations (SPDEs). Despite recent advances in deep learning for fluid mechanics, limited research has explored modelling stochastically driven flows - which play a crucial role in understanding a broad spectrum of phenomena, from jets on giant planets to ocean circulation and the variability of midlatitude weather. The model architecture consists of a stochastically-forced transformer, paired with a translation-equivariant autoencoder, that we demonstrate is capable of reproducing system dynamics across various integration periods. We demonstrate its effectiveness applied to a well-researched zonal jet system, with the neural network achieving a five-order-of-magnitude speedup compared to numerical integration. This facilitates the cost-effective generation of large ensembles, enabling the exploration of statistical questions concerning probabilities of spontaneous transition events.

引入了“随机潜在转换器”，这是一种用于随机偏微分方程（SPDE）高效减维建模的概率深度学习方法。通过将具有随机驱动的流体力学建模应用于研究已深入的纬向气流系统，展示了该神经网络相较于数值积分实现了5个数量级的加速，从而实现了成本效益较高的大集合生成，进而探索关于自发转变事件概率的统计问题。

随机潜在变换器：随机强迫区域性涡旋的高效建模