论文标题
通过多尺度图神经网络快速模拟环境流体力学
Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks
论文作者
论文摘要
数值模拟器是研究天然流体系统的重要工具，但它们的性能通常会限制实践中的应用。最近的机器学习方法表明了它们加速时空预测的能力，尽管仅具有适度的准确性。在这里，我们介绍了MultiScaleGnn，这是一种新型的多尺度图神经网络模型，用于学习在包含一系列长度尺度和复杂边界几何形状的问题中推断出不稳定的连续性力学。我们在对流问题和不可压缩的流体动力学上演示了这种方法，既是海洋和大气过程中的基本现象。我们的结果表明，用于长期时间模拟的新域几何形状和参数良好的外推。用多幕获得的模拟比受过训练的模拟速度快于两个和四个数量级。
Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability to accelerate spatio-temporal predictions, although, with only moderate accuracy in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph neural network model for learning to infer unsteady continuum mechanics in problems encompassing a range of length scales and complex boundary geometries. We demonstrate this method on advection problems and incompressible fluid dynamics, both fundamental phenomena in oceanic and atmospheric processes. Our results show good extrapolation to new domain geometries and parameters for long-term temporal simulations. Simulations obtained with MultiScaleGNN are between two and four orders of magnitude faster than those on which it was trained.
