S3GM: Learning spatiotemporal dynamics with a pretrained generative model

Datasets of Kuramoto-Sivashinsky equation (KSE) and Kolmogorov flow. Description of KSE data: Each file for KSE datasets contains 4 dimensions in the following order: (B*V)*T*X*C. Details are listed in the following table: B number of varying initial conditions V number of varying parameters T number of temporal frames X spatial resolution C number of variables in solution (C = 1 for KSE) values of parameter used to generate training dataset 1.0, 1.2, 1.4, 1.6, 1.8, 2.0, 2.2, 2.4, 2.6, 2.8, 3.0, 3.2, 3.4, 3.6, 3.8, 4.0, 4.2, 4.4, 4.6, 4.8, 5.0 values of parameter used to generate test dataset 1.1, 2.5, 3.2 Description of Kolmogorov flow data: Each file for Kolmogorov flow contains 5 dimensions inthe following order: (B*Re*K)*T*X*X*C. Details are listed in the following table: B number of varying initial conditions Re number of varying Reynolds numbers K number of varying source terms (controled by the value of k) T number of temporal frames X spatial resolution C number of variables in solution (C = 2 for Kolmogorov flow) values of Reynolds number used to generate training dataset 100, 150, 200, 250, 300, 350, 400, 450, 500, 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000, 1050 values of Reynolds number used to generate test dataset 50, 125, 575, 1100, 1500 values of k used to generate training dataset 2, 3, 4, 5, 6, 7, 8 values of k used to generate test dataset 2, 4, 6, 8 Pretrained checkpoints: The .zip file contains the pretrained checkpoints for KSE and Kolmogorov flow. Within the .zip file, the folder'kse_v0' is the checkpoint for KSE, and 'kol_v0' is the checkpoint for Kolmogorov flow. Source code: The source code is upload as Github repository in https://github.com/lzy12301/S3GM