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

@article{SanchisAlepuz2022TowardsRT,
  title={Towards Real Time Thermal Simulations for Design Optimization using Graph Neural Networks},
  author={H{\`e}lios Sanchis-Alepuz and Monika Stipsitz},
  journal={2022 IEEE Design Methodologies Conference (DMC)},
  year={2022},
  pages={1-6}
}
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… 

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References

SHOWING 1-10 OF 14 REFERENCES

Approximating the Steady-state Temperature of 3D Electronic Systems with Convolutional Neural Networks

A proof-of-concept study of the application of convolutional neural networks to accelerate thermal simulations, and a custom network architecture is proposed which captures the long-range correlations present in heat conduction problems.

Approximating the full-field temperature evolution in 3D electronic systems from randomized "Minecraft" systems

A fully convolutional network is applied and, thus, 3D systems of randomly located voxels with randomly assigned physical properties are designed and good generalization to electronic systems four times as large as the training systems is obtained.

Learning to Simulate Complex Physics with Graph Networks

A machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains, involving fluids, rigid solids, and deformable materials interacting with one another, and holds promise for solving a wide range of complex forward and inverse problems.

Fast Neural Network Emulation of Dynamical Systems for Computer Animation

This paper demonstrates the possibility of replacing the numerical simulation of nontrivial dynamic models with a dramatically more efficient "NeuroAnimator" that exploits neural networks that is one or two orders of magnitude faster than conventional numerical simulation.

The Graph Neural Network Model

A new neural network model, called graph neural network (GNN) model, that extends existing neural network methods for processing the data represented in graph domains, and implements a function tau(G,n) isin IRm that maps a graph G and one of its nodes n into an m-dimensional Euclidean space.

Layer Normalization

Training state-of-the-art, deep neural networks is computationally expensive. One way to reduce the training time is to normalize the activities of the neurons. A recently introduced technique called

Self-Normalizing Neural Networks

Self-normalizing neural networks (SNNs) are introduced to enable high-level abstract representations and it is proved that activations close to zero mean and unit variance that are propagated through many network layers will converge towards zero meanand unit variance -- even under the presence of noise and perturbations.

PyTorch: An Imperative Style, High-Performance Deep Learning Library

This paper details the principles that drove the implementation of PyTorch and how they are reflected in its architecture, and explains how the careful and pragmatic implementation of the key components of its runtime enables them to work together to achieve compelling performance.

Fast Graph Representation Learning with PyTorch Geometric

PyTorch Geometric is introduced, a library for deep learning on irregularly structured input data such as graphs, point clouds and manifolds, built upon PyTorch, and a comprehensive comparative study of the implemented methods in homogeneous evaluation scenarios is performed.

Relational inductive biases, deep learning, and graph networks

It is argued that combinatorial generalization must be a top priority for AI to achieve human-like abilities, and that structured representations and computations are key to realizing this objective.