PDE Control Gym

PDE Control Gym是由加州大学圣地亚哥分校开发的首个针对偏微分方程（PDE）边界控制的学习基准。该数据集包含三个基础PDE问题：一维传输PDE、一维反应-扩散PDE和二维Navier-Stokes PDE。数据集通过一个用户友好的强化学习环境实现，允许使用任何预先实现的机器学习算法进行PDE控制。此数据集旨在降低学习型PDE控制领域的入门门槛，并推动新控制方法的发展。

PDE Control Gym is the first learning benchmark for partial differential equation (PDE) boundary control, developed by the University of California, San Diego. This dataset encompasses three fundamental PDE problems: one-dimensional transport PDE, one-dimensional reaction-diffusion PDE, and two-dimensional Navier-Stokes PDE. Implemented via a user-friendly reinforcement learning environment, it enables the application of any pre-implemented machine learning algorithm for PDE control tasks. The core goal of this dataset is to reduce the barrier to entry for the field of learning-based PDE control and advance the development of novel control methodologies.