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

TL;DR

This paper introduces a graph neural network approach for real-time thermal simulation of 3D systems, achieving high accuracy and significant speed improvements over traditional finite-element methods, enabling faster design optimization.

Contribution

The paper presents a novel GNN-based method for fast thermal simulation of 3D systems, trained on diverse CAD data, with high accuracy and potential for real-time design applications.

Findings

One-step prediction error of 0.003%

After 400 steps, error reaches 0.78%

Each time step computed in 50 ms

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.