Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks
Abstract
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.
Cite
@article{arxiv.2209.13348,
title = {Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks},
author = {Helios Sanchis-Alepuz and Monika Stipsitz},
journal= {arXiv preprint arXiv:2209.13348},
year = {2022}
}
Comments
Presented at the Design Methodologies Conference 2022 (DMC2022) in Bath, England. 6 pages, 7 figures