A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables

• URL: http://arxiv.org/abs/2311.11343v3
• Date: Tue, 21 May 2024 14:01:23 GMT
• Title: A Generative Model for Accelerated Inverse Modelling Using a Novel Embedding for Continuous Variables
• Authors: Sébastien Bompas, Stefan Sandfeld,
• Abstract summary: In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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