Self-supervised feature distillation and design of experiments for efficient training of micromechanical deep learning surrogates

TL;DR

This paper introduces a self-supervised feature distillation method and experimental design strategies to improve the accuracy and efficiency of micromechanical deep learning surrogates, especially in selecting microstructural samples for training.

Contribution

It proposes a novel contrastive self-supervised feature extraction technique and microstructural experimental design criteria to enhance surrogate model performance in micromechanics.

Findings

Up to 8% improvement in surrogate accuracy with proposed strategies

Contrastive feature extraction enables automated microstructural summary statistics

Design strategies are more beneficial for larger problems

Abstract

Machine learning surrogate emulators are needed in engineering design and optimization tasks to rapidly emulate computationally expensive physics-based models. In micromechanics problems the local full-field response variables are desired at microstructural length scales. While there has been a great deal of work on establishing architectures for these tasks there has been relatively little work on establishing microstructural experimental design strategies. This work demonstrates that intelligent selection of microstructural volume elements for subsequent physics simulations enables the establishment of more accurate surrogate models. There exist two key challenges towards establishing a suitable framework: (1) microstructural feature quantification and (2) establishment of a criteria which encourages construction of a diverse training data set. Three feature extraction strategies are used as well as three design criteria. A novel contrastive feature extraction approach is established for automated self-supervised extraction of microstructural summary statistics. Results indicate that for the problem considered up to a 8\% improvement in surrogate performance may be achieved using the proposed design and training strategy. Trends indicate this approach may be even more beneficial when scaled towards larger problems. These results demonstrate that the selection of an efficient experimental design is an important consideration when establishing machine learning based surrogate models.