论文标题
DS-GPS：深度统计图泊松求解器（用于更快的CFD模拟）
DS-GPS : A Deep Statistical Graph Poisson Solver (for faster CFD simulations)
论文作者
论文摘要
本文提出了一种基于机器学习的新方法，以解决混合边界条件的泊松问题。利用图形神经网络，我们开发了一个模型，能够处理非结构化的网格，从而通过设计来实施边界条件。通过直接最大程度地减少泊松方程的残差，该模型试图在不需要精确解决方案的情况下学习问题的物理，与大多数以前的数据驱动的过程相比，使用可用解决方案的距离被最小化。
This paper proposes a novel Machine Learning-based approach to solve a Poisson problem with mixed boundary conditions. Leveraging Graph Neural Networks, we develop a model able to process unstructured grids with the advantage of enforcing boundary conditions by design. By directly minimizing the residual of the Poisson equation, the model attempts to learn the physics of the problem without the need for exact solutions, in contrast to most previous data-driven processes where the distance with the available solutions is minimized.
