R
Ramin Bostanabad
Researcher at University of California, Irvine
Publications - 35
Citations - 1610
Ramin Bostanabad is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Computer science & Gaussian process. The author has an hindex of 12, co-authored 23 publications receiving 885 citations. Previous affiliations of Ramin Bostanabad include University of California & Northwestern University.
Papers
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Journal ArticleDOI
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality
Miguel A. Bessa,Miguel A. Bessa,Ramin Bostanabad,Zeliang Liu,A. Hu,Daniel W. Apley,Catherine Brinson,Wei Chen,Wing Kam Liu +8 more
TL;DR: A new data-driven computational framework is developed to assist in the design and modeling of new material systems and structures and includes the recently developed “self-consistent clustering analysis” method in order to build large databases suitable for machine learning.
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Deep learning predicts path-dependent plasticity.
Mojtaba Mozaffar,Ramin Bostanabad,Ramin Bostanabad,Wei Chen,Kornel F. Ehmann,Jian Cao,Miguel A. Bessa +6 more
TL;DR: This work offers an alternative to currently established plasticity formulations by providing the foundations for finding history- and microstructure-dependent constitutive models through deep learning.
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Computational microstructure characterization and reconstruction: Review of the state-of-the-art techniques
Ramin Bostanabad,Yichi Zhang,Xiaolin Li,Tucker Kearney,L. Catherine Brinson,Daniel W. Apley,Wing Kam Liu,Wei Chen +7 more
TL;DR: A comprehensive review of representative approaches for MCR and elaborate on their algorithmic details, computational costs, and how they fit into the PSP mapping problems is provided.
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Stochastic microstructure characterization and reconstruction via supervised learning
TL;DR: The main advantages of the approach stem from having a compact empirically-learned model that characterizes the stochastic nature of the microstructure, which not only makes reconstruction more computationally efficient than existing methods, but also provides insight into morphological complexity.
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Uncertainty quantification in multiscale simulation of woven fiber composites
Ramin Bostanabad,Biao Liang,Jiaying Gao,Wing Kam Liu,Jian Cao,Danielle Zeng,Xuming Su,Hongyi Xu,Yang Li,Wei Chen +9 more
TL;DR: The top-down sampling method is introduced that allows to model non-stationary and continuous (but not differentiable) spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF.