Efficiently modeling spatio-temporal (ST) physical processes and observations presents a challenging problem for the deep learning community. Many recent studies have concentrated on meticulously reconciling various advantages, leading to designed models that are neither simple nor practical. To address this issue, this paper presents a systematic study on existing shortcomings faced by off-the-shelf models, including lack of local fidelity, poor prediction performance over long time-steps,low scalability, and inefficiency. To systematically address the aforementioned problems, we propose an EarthFarseer, a concise framework that combines parallel local convolutions and global Fourier-based transformer architectures, enabling dynamically capture the local-global spatial interactions and dependencies. EarthFarseer also incorporates a multi-scale fully convolutional and Fourier architectures to efficiently and effectively capture the temporal evolution. Our proposal demonstrates strong adaptability across various tasks and datasets, with fast convergence and better local fidelity in long time-steps predictions. Extensive experiments and visualizations over eight human society physical and natural physical datasets demonstrates the state-of-the-art performance of EarthFarseer. We release our code at https://github.com/easylearningscores/EarthFarseer.

翻译：高效建模时空物理过程与观测数据是深度学习领域面临的一项挑战。许多近期研究专注于精心调和各类优势，导致所设计的模型既不够简洁也不够实用。为应对这一问题，本文系统研究了现有通用模型面临的缺陷，包括局部保真度不足、长时步预测性能差、可扩展性低以及效率低下。为系统解决上述问题，我们提出EarthFarseer——一个融合并行局部卷积与基于全局傅里叶变换的Transformer架构的简洁框架，能够动态捕捉局部-全局空间交互与依赖关系。EarthFarseer还结合了多尺度全卷积与傅里叶架构，以高效且有效地捕捉时间演化规律。我们的方案在多种任务与数据集上展现出强大的适应性，在长时步预测中具有快速收敛和更优的局部保真度。在八个人类社会物理与自然物理数据集上的大量实验与可视化结果证明了EarthFarseer的先进性能。代码已发布于https://github.com/easylearningscores/EarthFarseer。