基于物理信息的像素级自注意力生成对抗网络实现风场三维超分辨率
A 3D super-resolution of wind fields via physics-informed pixel-wise self-attention generative adversarial network
AI总结：
本文提出一种物理信息像素级自注意力生成对抗网络，通过9倍放大实现三维风场超分辨率，降低计算成本89.7倍。
摘要：
为缓解全球变暖，温室气体源需要以高空间分辨率进行解析并实时监测，以确保污染源的减少并最终消除。然而，解析高分辨率风场的计算复杂性使得模拟难以测试不同的时间长度和模型配置。本研究初步开发了一种物理信息超分辨率（SR）生成对抗网络（GAN），通过放大9倍来超分辨率重建三维（3D）低分辨率风场。我们开发了一个像素级自注意力（PWA）模块，通过自注意力计算后接二维卷积来学习三维天气动力学。我们还在预训练期间采用一个损失项来正则化自注意力图，从输入风数据中捕获垂直对流过程。新的PWA SR-GAN展示了高保真度的超分辨率三维风数据，学习了高频域中的风结构，并将高分辨率风模拟的计算成本降低了89.7倍。
英文摘要：
To mitigate global warming, greenhouse gas sources need to be resolved at a high spatial resolution and monitored in time to ensure the reduction and ultimately elimination of the pollution source. However, the complexity of computation in resolving high-resolution wind fields left the simulations impractical to test different time lengths and model configurations. This study presents a preliminary development of a physics-informed super-resolution (SR) generative adversarial network (GAN) that super-resolves the three-dimensional (3D) low-resolution wind fields by upscaling x9 times. We develop a pixel-wise self-attention (PWA) module that learns 3D weather dynamics via a self-attention computation followed by a 2D convolution. We also employ a loss term that regularizes the self-attention map during pretraining, capturing the vertical convection process from input wind data. The new PWA SR-GAN shows the high-fidelity super-resolved 3D wind data, learns a wind structure at the high-frequency domain, and reduces the computational cost of a high-resolution wind simulation by x89.7 times.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
