@inproceedings{
bompas2023a,
title={A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables},
author={Sebastien Bompas and Stefan Sandfeld},
booktitle={AI for Accelerated Materials Design - NeurIPS 2023 Workshop},
year={2023},
url={https://openreview.net/forum?id=5ioOONby01}
}
In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Additionally, finding microstructures for given properties is typically an ill-posed problem where multiple solutions may exist. Using generative machine learning models can be a viable solution which also reduces the computational cost. This comes with new challenges because, e.g., a continuous property variable as conditioning input to the model is required. We investigate the shortcomings of an existing method and compare this to a novel embedding strategy for generative models that is based on the binary representation of floating point numbers. This eliminates the need for normalization, preserves information, and creates a versatile embedding space for conditioning the generative model. This technique can be applied to condition a network on any number, to provide fine control over generated microstructure images, thereby contributing to accelerated materials design.
A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables
Sebastien Bompas Stefan Sandfeld