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Brian Phung
Brian Phung
Post Doctoral Research Associate, University of Utah
Verified email at utah.edu
Title
Cited by
Cited by
Year
A convected-particle tetrahedron interpolation technique in the material-point method for the mesoscale modeling of ceramics
RB Leavy, JE Guilkey, BR Phung, AD Spear, RM Brannon
Computational Mechanics 64, 563-583, 2019
182019
Investigating the effect of grain structure on compressive response of open-cell metal foam using high-fidelity crystal-plasticity modeling
D Zhao, KE Matheson, BR Phung, S Petruzza, MW Czabaj, AD Spear
Materials Science and Engineering: A 812, 140847, 2021
122021
Implementation and experimental validation of nonlocal damage in a large-strain elasto-viscoplastic FFT-based framework for predicting ductile fracture in 3D polycrystalline …
CK Cocke, H Mirmohammad, M Zecevic, BR Phung, RA Lebensohn, ...
International Journal of Plasticity 162, 103508, 2023
92023
The third Sandia Fracture Challenge: from theory to practice in a classroom setting
AD Spear, MW Czabaj, P Newell, K DeMille, BR Phung, D Zhao, ...
International Journal of Fracture 218, 171-194, 2019
82019
A voxel-based remeshing framework for the simulation of arbitrary three-dimensional crack growth in heterogeneous materials
BR Phung, AD Spear
Engineering Fracture Mechanics 209, 404-422, 2019
82019
A surface-mesh gradation tool for generating gradated tetrahedral meshes of microstructures with defects
BR Phung, J He, AD Spear
Computational Materials Science 197, 110622, 2021
42021
Predicting microstructurally sensitive fatigue‐crack path in WE43 magnesium using high‐fidelity numerical modeling and three‐dimensional experimental characterization
BR Phung, DA Greeley, M Yaghoobi, JF Adams, JE Allison, AD Spear
Fatigue & Fracture of Engineering Materials & Structures 47 (3), 862-883, 2024
12024
Interpretability and Generalizability of Constitutive Models using Symbolic Regression
J Hochhalter, K Garbrecht, D Birky, D Randall, B Phung, N Strauss, ...
153rd TMS Annual Meeting & Exhibition, 2024
2024
Simulation of Three-Dimensional Microstructurally Sensitive Crack Growth Using a Voxel-Based Remeshing Framework
BR Phung
The University of Utah, 2022
2022
Predicting the yield behavior of polycrystalline aggregates usinga generalized Schmid factor approach.
C Alleman, B Phung
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021
2021
An analytical model for material strength accounting for microstructural variability.
B Phung, C Alleman
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2020
2020
Towards the Verification of a Generalized Schmid-and Taylor-factor Homogenization Scheme and Mesoscale Mesh Generation from Noisy Image Data.
B Phung, C Alleman
Sandia National Lab.(SNL-CA), Livermore, CA (United States), 2019
2019
Predicting Fall Parameters from Infant Skull Fractures Using Machine Learning
J Hirst, B Phung, B Johnsson, J He, B Coats, A Spear
Available at SSRN 4725023, 0
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Articles 1–13