Many real-world processes have complex tail dependence structures that cannot be characterized using classical Gaussian processes. More flexible spatial extremes models such as Gaussian scale mixtures and single-station conditioning models exhibit appealing extremal dependence properties but are often exceedingly prohibitive to fit and simulate from. In this paper, we develop a new spatial extremes model that has flexible and non-stationary dependence properties, and we integrate it in the encoding-decoding structure of a variational autoencoder (extVAE). The extVAE can be used as a spatio-temporal emulator that characterizes the distribution of potential mechanistic model output states and produces outputs that have the same properties as the inputs, especially in the tail. Through extensive simulation studies, we show that our extVAE is vastly more time-efficient than traditional Bayesian inference while also outperforming many spatial extremes models with a stationary dependence structure. To further demonstrate the computational power of the extVAE, we analyze a high-resolution satellite-derived dataset of sea surface temperature in the Red Sea, which includes daily measurements at 16703 grid cells.

通过构建一个新的可变空间特异极值自动编码器模型(extVAE)，本文研究了一种具有灵活非平稳依赖性的极值分布，并在 Red Sea 海域的高分辨率卫星获取的温度数据中进行了计算。

可变自编码器在空间极值模拟中的灵活高效应用