论文标题
使用图神经网络进行实时热模拟进行设计优化
Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks
论文作者
论文摘要
本文提出了一种使用图神经网络模拟3D系统的热行为的方法。讨论的方法对传统的有限元模拟实现了显着的加速。图形神经网络在3D CAD设计的各种数据集和相应的有限元模拟的数据集上进行了训练，该模拟代表了电子系统设计中出现的不同几何，材料属性和损失。我们为测试系统的瞬时热行为提供了展示。一步预测的网络结果的准确性非常明显（\ si {0.003} {\％}错误）。 400个时间步长后，累积错误到达\ si {0.78} {\％}。每个时间步的计算时间为\ si {50} {MS}。减少累积错误是我们工作的当前重点。将来，我们呈现的工具可以提供可用于设计优化的系统的热行为的瞬时近似值。
This paper presents a method to simulate the thermal behavior of 3D systems using a graph neural network. The method discussed achieves a significant speed-up with respect to a traditional finite-element simulation. The graph neural network is trained on a diverse dataset of 3D CAD designs and the corresponding finite-element simulations, representative of the different geometries, material properties and losses that appear in the design of electronic systems. We present for the transient thermal behavior of a test system. The accuracy of the network result for one-step predictions is remarkable (\SI{0.003}{\%} error). After 400 time steps, the accumulated error reaches \SI{0.78}{\%}. The computing time of each time step is \SI{50}{ms}. Reducing the accumulated error is the current focus of our work. In the future, a tool such as the one we are presenting could provide nearly instantaneous approximations of the thermal behavior of a system that can be used for design optimization.
