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G.X. Gu, C.-T. Chen, D.J. Richmond, and M.J. Buehler: Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater. Horiz. 5, 939 (2018).
DOI
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2 |
G.X. Gu, C.-T. Chen, and M.J. Buehler: De novo composite design based on machine learning algorithm. Extreme Mech. Lett. 18, 19 (2018).
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3 |
Yongtae Kim, Charles Yang, Youngsoo Kim, Grace Gu, and Seunghwa Ryu*, "Designing Adhesive Pillar Shape with Deep Learning-Based Optimization", ACS Applied Materials & Interface accepted.
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S. Tiryaki and A. Aydirn: An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Constr. Build. Mater. 62, 102 (2014).
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F. Khademi, M. Akbari, S.M. Jamal, and M Nikoo: Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front. Struct. Civil Eng. 11, 90 (2017).
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6 |
B.A. Young, A, Hall, L, Pilon, P. Gupta, and G. Sant: Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods. Cem. Concr. Res. 115, 379 (2019).
DOI
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7 |
G. Tapia, A. Elwany, and H. Sang: Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Addit. Manuf. 12, 282 (2016).
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8 |
Heeyeong Jeong, Stefano Signetti, Tong-seok Han, and Seunghwa Ryu* "Phase field modeling of crack propagation under combined shear and tensile loading with hybrid formulation", Computational Materials Science 155, 438 (2018).
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Charles Yang+, Youngsoo Kim+, Seunghwa Ryu*, and Grace Gu*, "Using Convolutional Neural Networks to Predict Composite Properties beyond the Elastic Limit", MRS Communications 9, 609 (2019).
DOI
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10 |
Charles Yang+, Youngsoo Kim+, Seunghwa Ryu*, and Grace Gu*, "Prediction of composite microstructure stress-strain curves using convolutional neural networks", Materials & Design 189, 108509 (2020).
DOI
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