Artikel mit Open-Access-Mandaten - Yuksel C. YabansuWeitere Informationen
Nicht verfügbar: 4
Analytics for microstructure datasets produced by phase-field simulations
P Steinmetz, YC Yabansu, J Hötzer, M Jainta, B Nestler, SR Kalidindi
Acta Materialia 103, 192-203, 2016
Mandate: Deutsche Forschungsgemeinschaft
A new framework for rotationally invariant two-point spatial correlations in microstructure datasets
A Cecen, YC Yabansu, SR Kalidindi
Acta Materialia 158, 53-64, 2018
Mandate: US National Science Foundation
High-throughput exploration of the process space in 18% Ni (350) maraging steels via spherical indentation stress–strain protocols and Gaussian process models
S Parvinian, YC Yabansu, A Khosravani, H Garmestani, SR Kalidindi
Integrating Materials and Manufacturing Innovation 9, 199-212, 2020
Mandate: US Department of Defense
Data Analytics on Phase-Field Simulation Datasets
YC Yabansu, SR Kalidindi
HANDBOOK ON BIG DATA AND MACHINE LEARNING IN THE PHYSICAL SCIENCES: Volume 1 …, 2020
Mandate: US Department of Defense
Verfügbar: 13
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
Z Yang, YC Yabansu, R Al-Bahrani, W Liao, AN Choudhary, SR Kalidindi, ...
Computational Materials Science 151, 278-287, 2018
Mandate: US National Science Foundation, US Department of Energy, US Department of …
Material structure-property linkages using three-dimensional convolutional neural networks
A Cecen, H Dai, YC Yabansu, SR Kalidindi, L Song
Acta Materialia 146, 76-84, 2017
Mandate: US National Science Foundation, US Department of Defense, US National …
Establishing structure-property localization linkages for elastic deformation of three-dimensional high contrast composites using deep learning approaches
Z Yang, YC Yabansu, D Jha, W Liao, AN Choudhary, SR Kalidindi, ...
Acta Materialia 166, 335-345, 2019
Mandate: US National Science Foundation, US Department of Energy, US Department of …
Machine learning approaches for elastic localization linkages in high-contrast composite materials
R Liu, YC Yabansu, A Agrawal, SR Kalidindi, AN Choudhary
Integrating Materials and Manufacturing Innovation 4, 192-208, 2015
Mandate: US Department of Energy
Extraction of reduced-order process-structure linkages from phase-field simulations
YC Yabansu, P Steinmetz, J Hötzer, SR Kalidindi, B Nestler
Acta Materialia 124, 182-194, 2017
Mandate: US Department of Defense, Deutsche Forschungsgemeinschaft
Quantification and classification of microstructures in ternary eutectic alloys using 2-point spatial correlations and principal component analyses
A Choudhury, YC Yabansu, SR Kalidindi, A Dennstedt
Acta Materialia 110, 131-141, 2016
Mandate: Deutsche Forschungsgemeinschaft
Application of Gaussian process regression models for capturing the evolution of microstructure statistics in aging of nickel-based superalloys
YC Yabansu, A Iskakov, A Kapustina, S Rajagopalan, SR Kalidindi
Acta Materialia 178, 45-58, 2019
Mandate: US Department of Defense
Context aware machine learning approaches for modeling elastic localization in three-dimensional composite microstructures
R Liu, YC Yabansu, Z Yang, AN Choudhary, SR Kalidindi, A Agrawal
Integrating Materials and Manufacturing Innovation 6, 160-171, 2017
Mandate: US National Science Foundation, US Department of Energy, US Department of …
Data Science Approaches for Microstructure Quantification and Feature Identification in Porous Membranes
P Altschuh, YC Yabansu, J Hötzer, M Selzer, B Nestler, SR Kalidindi
Journal of Membrane Science 540, 88-97, 2017
Mandate: US Department of Defense, Bundesministerium für Bildung und Forschung
Application of Gaussian process autoregressive models for capturing the time evolution of microstructure statistics from phase-field simulations for sintering of …
YC Yabansu, V Rehn, J Hötzer, B Nestler, SR Kalidindi
Modelling and Simulation in Materials Science and Engineering 27 (8), 084006, 2019
Mandate: Bundesministerium für Bildung und Forschung
A digital workflow for learning the reduced-order structure-property linkages for permeability of porous membranes
YC Yabansu, P Altschuh, J Hötzer, M Selzer, B Nestler, SR Kalidindi
Acta Materialia 195, 668-680, 2020
Mandate: US Department of Defense, Deutsche Forschungsgemeinschaft, Helmholtz …
A comparative study of the efficacy of local/global and parametric/nonparametric machine learning methods for establishing structure–property linkages in high-contrast 3D …
P Fernandez-Zelaia, YC Yabansu, SR Kalidindi
Integrating Materials and Manufacturing Innovation 8 (2), 67-81, 2019
Mandate: US National Science Foundation, US Department of Energy
Evaluation of Ti–Mn alloys for additive manufacturing using high-throughput experimental assays and gaussian process regression
X Gong, YC Yabansu, PC Collins, SR Kalidindi
Materials 13 (20), 4641, 2020
Mandate: US National Science Foundation, US Department of Defense
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