TITLE: Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset 

ABSTRACT: Modeling biological soft tissue is complex in part due to material
heterogeneity. Microstructural patterns, which play a major role in defining
the mechanical behavior of these tissues, are both challenging to characterize,
and difficult to simulate. Recently, machine learning-based methods to predict
the mechanical behavior of heterogeneous materials have made it possible to
more thoroughly explore the massive input parameter space associated with
heterogeneous blocks of material. Specifically, we can train machine learning
(ML) models to closely approximate computationally expensive heterogeneous
material simulations where the ML model is trained on a dataset of simulations
that capture the range of spatial heterogeneity present in the material of
interest. However, when it comes to applying these techniques to biological
tissue more broadly, there is a major limitation: the relevant microstructural
patterns are both challenging to obtain and difficult to analyze. Consequently,
the number of useful examples available to characterize the input domain under
study is limited. In this work, we investigate the efficacy of ML-based
generative models as well as procedural methods as a tool for augmenting
limited input pattern datasets. We find that a Style-based Generative
Adversarial Network with adaptive discriminator augmentation is able to
successfully leverage just 1,000 example patterns to create the most authentic
generated patterns. In general, diverse generated patterns with adequate
resemblance to the real patterns can be used as inputs to finite element
simulations to meaningfully augment the training dataset. To enable this
methodological contribution, we have created an open access dataset of Finite
Element Analysis simulations based on Cahn-Hilliard patterns. We anticipate
that future researchers will be able to leverage this dataset and build on the
work presented here.
Modeling biological soft tissue is complex in part due to material
heterogeneity. Microstructural patterns, which play a major role in defining
the mechanical behavior of these tissues, are both challenging to characterize,
and difficult to simulate. Recently, machine learning-based methods to predict
the mechanical behavior of heterogeneous materials have made it possible to
more thoroughly explore the massive input parameter space associated with
heterogeneous blocks of material. Specifically, we can train machine learning
(ML) models to closely approximate computationally expensive heterogeneous
material simulations where the ML model is trained on a dataset of simulations
that capture the range of spatial heterogeneity present in the material of
interest. However, when it comes to applying these techniques to biological
tissue more broadly, there is a major limitation: the relevant microstructural
patterns are both challenging to obtain and difficult to analyze. Consequently,
the number of useful examples available to characterize the input domain under
study is limited. In this work, we investigate the efficacy of ML-based
generative models as well as procedural methods as a tool for augmenting
limited input pattern datasets. We find that a Style-based Generative
Adversarial Network with adaptive discriminator augmentation is able to
successfully leverage just 1,000 example patterns to create the most authentic
generated patterns. In general, diverse generated patterns with adequate
resemblance to the real patterns can be used as inputs to finite element
simulations to meaningfully augment the training dataset. To enable this
methodological contribution, we have created an open access dataset of Finite
Element Analysis simulations based on Cahn-Hilliard patterns. We anticipate
that future researchers will be able to leverage this dataset and build on the
work presented here.
Enhancing Mechanical Metamodels with a Generative Model-Based Augmented Training Dataset
http://arxiv.org/abs/2203.04183v3