Search Results for author: Elizabeth A. Holm

Found 5 papers, 1 papers with code

Microstructure Generation via Generative Adversarial Network for Heterogeneous, Topologically Complex 3D Materials

no code implementations22 Jun 2020 Tim Hsu, William K. Epting, Hokon Kim, Harry W. Abernathy, Gregory A. Hackett, Anthony D. Rollett, Paul A. Salvador, Elizabeth A. Holm

Using a large-scale, experimentally captured 3D microstructure dataset, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes.

Generative Adversarial Network

Overview: Computer vision and machine learning for microstructural characterization and analysis

no code implementations28 May 2020 Elizabeth A. Holm, Ryan Cohn, Nan Gao, Andrew R. Kitahara, Thomas P. Matson, Bo Lei, Srujana Rao Yarasi

The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties.

BIG-bench Machine Learning Image Classification +4

High throughput quantitative metallography for complex microstructures using deep learning: A case study in ultrahigh carbon steel

3 code implementations4 May 2018 Brian L. DeCost, Bo Lei, Toby Francis, Elizabeth A. Holm

We apply a deep convolutional neural network segmentation model to enable novel automated microstructure segmentation applications for complex microstructures typically evaluated manually and subjectively.

Segmentation

A comparative study of feature selection methods for stress hotspot classification in materials

no code implementations19 Apr 2018 Ankita Mangal, Elizabeth A. Holm

The first step in constructing a machine learning model is defining the features of the data set that can be used for optimal learning.

BIG-bench Machine Learning feature selection +1

Building Data-driven Models with Microstructural Images: Generalization and Interpretability

no code implementations1 Nov 2017 Julia Ling, Maxwell Hutchinson, Erin Antono, Brian DeCost, Elizabeth A. Holm, Bryce Meredig

As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure.

BIG-bench Machine Learning

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