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For multilayer materials in thin substrate systems, interfacial failure is one of the most challenges.
Cohesive zone models: a critical review of traction-separation relationships across fracture surfaces
Kyoungsoo Park and Glaucio H Paulino · 2011
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Deep learning
Yann LeCun, Yoshua Bengio, and Geoffrey Hinton · 2015
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Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
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On determining mixed-mode traction–separation relations for interfaces
Chenglin Wu, Shravan Gowrishankar, Rui Huang, and Kenneth M Liechti · 2016
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Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
Earlier work this paper cites.
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
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The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets
Jiaxin Zhang and Michael D Shields · 2018
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On the quantification and efficient propagation of imprecise probabilities resulting from small datasets
Jiaxin Zhang and Michael D Shields · 2018
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Scalable global optimization via local bayesian optimization
David Eriksson, Michael Pearce, Jacob Gardner, Ryan D Turner, and Matthias Poloczek · 2019
Cited alongside, same era.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
Cited alongside, same era.
Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning
Kun Wang and WaiChing Sun · 2019
Cited alongside, same era.
Simultaneous extraction of tensile and shear interactions at interfaces
Chenglin Wu, Rui Huang, and Kenneth M Liechti · 2019
Cited alongside, same era.
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems
Dongkun Zhang, Lu Lu, Ling Guo, and George Em Karniadakis · 2019
Later among the works it cites.
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Yuyao Chen, Lu Lu, George Em Karniadakis, and Luca Dal Negro · 2020
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Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4d flow mri data using physics-informed neural networks
Georgios Kissas, Yibo Yang, Eileen Hwuang, Walter R Witschey, John A Detre, and Paris Perdikaris · 2020
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Physics-informed neural networks for high-speed flows
Zhiping Mao, Ameya D Jagtap, and George Em Karniadakis · 2020
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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations
Maziar Raissi, Alireza Yazdani, and George Em Karniadakis · 2020
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Rate-dependent traction-separation relations for a silicon/epoxy interface informed by experiments and bond rupture kinetics
Tianhao Yang, Xingwei Yang, Rui Huang, and Kenneth M Liechti · 2019
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Monte carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach
Xianglin Liu, Jiaxin Zhang, Junqi Yin, Sirui Bi, Markus Eisenbach, and Yang Wang
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A directional gaussian smoothing optimization method for computational inverse design in nanophotonics
Jiaxin Zhang, Sirui Bi, and Guannan Zhang
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Robust data-driven approach for predicting the configurational energy of high entropy alloys
Jiaxin Zhang, Xianglin Liu, Sirui Bi, Junqi Yin, Guannan Zhang, and Markus Eisenbach · 2020
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