Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks

• URL: http://arxiv.org/abs/2209.13348v1
• Date: Wed, 21 Sep 2022 13:48:41 GMT
• Title: Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks
• Authors: Helios Sanchis-Alepuz and Monika Stipsitz
• Abstract summary: We present a method to simulate the thermal behavior of 3D systems using a graph neural network. The accuracy of the network result for one-step predictions is remarkable.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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