PDEBench Pretrained Models

This dataset contains the pretrained baseline models, namely FNO, U-Net, and PINN. These models are trained on different PDEs, such as 1D advection, 1D Burgers', 1D and 2D diffusion-reaction, 1D diffusion-sorption, 1D, 2D, and 3D compressible Navier-Stokes, 2D Darcy flow, and 2D shallow water equation. In addition the dataset contains the pre-trained model for the 1D Inverse problem for FNO and U-Net. These models are stored using the same structure as the dataset they trained on. All the files are saved in .pt files, the default file type for the PyTorch library. More detailed information are also provided in our Github repository (https://github.com/pdebench/PDEBench) and our submitting paper to NeurIPS 2022 Benchmark track.

本数据集包含若干预训练基准模型，具体包括傅里叶神经算子（FNO）、U型网络（U-Net）与物理信息神经网络（PINN）。上述模型均针对各类偏微分方程（PDEs）完成训练，涵盖一维平流方程、一维伯格斯方程、一维与二维扩散反应方程、一维扩散吸附方程、一维、二维及三维可压缩纳维-斯托克斯方程、二维达西流方程以及二维浅水波方程。此外，本数据集还收录了面向FNO与U-Net的一维逆问题预训练模型。所有模型的存储结构均与其训练所依托的数据集结构保持一致。全部文件均以.pt格式存储，这是PyTorch库的默认文件类型。更详尽的相关信息可参阅我们的GitHub仓库（https://github.com/pdebench/PDEBench）以及我们投稿至NeurIPS 2022基准赛道的学术论文。

搜集汇总

背景与挑战

背景概述

该数据集提供了针对多种偏微分方程预训练的基准模型（FNO、U-Net和PINN），覆盖1D和2D等多种场景，文件以PyTorch的.pt格式保存，适用于科学机器学习领域的基准测试和研究。