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publications

Automated Segmentation and Chord Length Distribution of Melt Pools in Complex 3D Printed Metal Artifacts.

Published in Integrated Materials and Manufacturing Innovation, 2023

Use Springer Link for full citation

Recommended citation: Whitman, Sheila E., Guangyu Hu, Hunter C. Taylor, Ryan B. Wicker, and Marat I. Latypov. (2023). "Automated Segmentation and Chord Length Distribution of Melt Pools in Complex 3D Printed Metal Artifacts." Integrated Materials and Manufacturing Innovation.

research

talks

Semi-Extrapolated Finite Difference Schemes: Accuracy and Consistency

Published: November 19, 2018

When solving partial differential equations, finite difference methods are a popular choice. Several factors come into play when choosing a finite difference method, such as stability, computational cost, accuracy, and consistency. In response to the small stability regions of explicit methods and the computational cost of implicit methods, we’ve developed a novel discretization technique (semi-extrapolation) that generates explicit schemes from implicit schemes by applying extrapolation to the implicit schemes in an unconventional fashion. Semi-extrapolating can lead to improved stabilities as compared to the stabilities of analogous explicit schemes, however, consistency and accuracy can be affected by semi-extrapolation. In our presentation, we’ll discuss our semi-extrapolation technique and introduce several semi-extrapolated discretizations of the Advection Equation and the Advection-Diffusion Equation. We’ll then analyze the consistency of these semi-extrapolated discretizations, compare their accuracies against the accuracies of several common discretizations, and discuss how stability constraints and choice of extrapolation stencil both influence the consistency and accuracy of semi-extrapolated schemes.

Accuracy and Computational Cost of Semi-Extrapolated Finite Difference Schemes

Published: November 25, 2019

When numerically solving partial differential equations, finite difference methods are a popular choice. Several factors come into play when choosing a finite difference method, such as stability, accuracy, and computational cost. In response to the small stability regions of explicit methods and the computational cost of implicit methods, we’ve developed a novel discretization technique called semi-extrapolation. Semi-extrapolation generates explicit schemes from implicit schemes by applying extrapolation in an unconventional fashion. Semi-extrapolation can improve stability, however, we’ve also found that semi-extrapolation can have unexpected and interesting effects on accuracy. In our presentation, we’ll introduce our semi-extrapolation technique and discretize the Advection Equation and the Advection-Diffusion Equation according to semi-extrapolated and mainstream finite difference methods. Then, we’ll examine the computational costs and accuracies of semi-extrapolated methods. Included in this examination will be a comparison against the costs and accuracies of mainstream methods and a discussion regarding how stability influences the accuracy of semi-extrapolated schemes.

Learning microstructure–property relationships in materials with robust features from vision transformers

Published: June 18, 2024

Machine learning of microstructure–property relation- ships from data is an emerging approach in computational materials science. Most existing machine learning efforts focus on the development of task-specific models for each microstructure–property relationship. We propose utilizing a pre-trained foundational vision model for the extraction of task-agnostic microstructure features and subsequent light- weight machine learning. We demonstrate our approach with a pre-trained DinoV2 model on unsupervised repre- sentation of an ensemble of two-phase microstructures and modeling of their overall elastic stiffness. Our results show the potential of foundational vision models for robust mi- crostructure representation and efficient machine learning of microstructure–property relationships without the need for expensive task-specific training or fine-tuning.

Sheila Whitman