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.
翻译:本文展示了使用图形神经网络模拟 3D 系统的热行为的方法。 所讨论的方法在传统的有限元素模拟中取得了显著的加速。 图形神经网络在3D CAD 设计和相应的有限元素模拟的多种数据集方面接受了培训, 代表了电子系统设计中出现的不同地形、 物质属性和损失。 我们为测试系统的瞬时热行为提供了一种方法。 单步预测的网络结果的准确性是惊人的 (\ SI{ 0.003 ⁇ 错误)。 400个步骤后, 累积的错误达到 \ SI{ 0. 78 ⁇ 。 每个步骤的计算时间是 \ SI{ 50\ ms } 。 减少累积的错误是我们当前工作的重点。 今后, 我们所展示的工具可以提供可用于设计优化的系统热行为的近乎即时近近近近近接近。