Out of Domain Stress Prediction on a Dataset of Simulated 3D Polycrystalline Microstructures

Surrogate machine learning models for expensive material simulations can be an effective method to estimate relevant properties, which can help reduce the number of experiments needed. However, significant difficulties can occur when attempting to learn from small simulated datasets, particularly for samples out of the domain of the training data. This work provides an exploration on training deep learning models on a dataset of 36 synthetic 3D equiaxed polycrystalline microstructures with different cubic textures with a focus on out-of-domain accuracy, analyzing a number of transfer learning set ups, domain adaptation methods, model architectures, and featurizations across two formulations of the problem. We develop an evaluation set-up to validate our results, and report several methods that provide better results than our baseline of a simple U-Net architecture.