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Data-Driven Materials Investigations: The Next Frontier in Understanding and Predicting Fatigue Behavior
Ashley D. Spear, Surya R. Kalidindi, Bryce Meredig, Antonios Kontsos, Jean‐Briac le Graverend
University of Utah Georgia Institute of Technology Citrine Informatics (United States) Drexel University
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学术脉络
学科主题
材料 / 化学Machine Learning in Materials Science
Fatigue and fracture mechanics · Elasticity and Material Modeling
参考文献 35
A computer vision approach for automated analysis and classification of microstructural image data
被引 327Brian DeCost, Elizabeth A. Holm · Computational Materials Science · 2015
The Fourth Paradigm – Data-Intensive Scientific Discovery
被引 1,641Tony Hey · Communications in computer and information science · 2012
Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties
被引 216Michael W. Gaultois, Anton O. Oliynyk, Arthur Mar · APL Materials · 2016
此处列出前 3 条
引用本文 36
A Review of the Application of Machine Learning and Data Mining Approaches in Continuum Materials Mechanics
被引 404Frederic E. Bock, Roland Can Aydin, Christian Johannes Cyron · Frontiers in Materials · 2019
Deep learning for topology optimization of 2D metamaterials
被引 360Hunter T. Kollmann, Diab Abueidda, Seid Korić · Materials & Design · 2020
Deep learning for plasticity and thermo-viscoplasticity
被引 213Diab Abueidda, Seid Korić, Nahil Sobh · International Journal of Plasticity · 2020
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