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

arXiv:2311.11343v2 Announce Type: replace
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 …

abstract arxiv challenge cond-mat.mtrl-sci continuous cs.lg embedding experimentation generative machine machine learning machine learning models materials materials science modelling multiple novel prototyping science solutions type variables