Yue Yu
Yue Yu
Professor of Applied Mathematics, Lehigh University
Verified email at - Homepage
Cited by
Cited by
DAG-GNN: DAG structure learning with graph neural networks
Y Yu, J Chen, T Gao, M Yu
International Conference on Machine Learning, 7154-7163, 2019
Variability of computational fluid dynamics solutions for pressure and flow in a giant aneurysm: the ASME 2012 Summer Bioengineering Conference CFD Challenge
DA Steinman, Y Hoi, P Fahy, L Morris, MT Walsh, N Aristokleous, ...
Journal of biomechanical engineering 135 (2), 021016, 2013
A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials
S Goswami, M Yin, Y Yu, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 391, 114587, 2022
Immersogeometric cardiovascular fluid–structure interaction analysis with divergence-conforming B-splines
D Kamensky, MC Hsu, Y Yu, JA Evans, MS Sacks, TJR Hughes
Computer methods in applied mechanics and engineering 314, 408-472, 2017
The non-symmetric Nitsche method for the parameter-free imposition of weak boundary and coupling conditions in immersed finite elements
YZ Dominik Schillinger, Isaac Harari, Ming-Chen Hsu, David Kamensky, Stein K ...
Computer Methods in Applied Mechanics and Engineering 309, 625-652, 2016
Fractional modeling of viscoelasticity in 3D cerebral arteries and aneurysms
Y Yu, P Perdikaris, GE Karniadakis
Journal of computational physics 323, 219-242, 2016
An asymptotically compatible meshfree quadrature rule for nonlocal problems with applications to peridynamics
N Trask, H You, Y Yu, ML Parks
Computer Methods in Applied Mechanics and Engineering 343, 151-165, 2019
Data-driven learning of nonlocal physics from high-fidelity synthetic data
H You, Y Yu, N Trask, M Gulian, M D’Elia
Computer Methods in Applied Mechanics and Engineering 374, 113553, 2021
Generalized fictitious methods for fluid–structure interactions: analysis and simulations
Y Yu, H Baek, GE Karniadakis
Journal of Computational Physics 245, 317-346, 2013
Suppression of vortex-induced vibrations by fairings: a numerical study
Y Yu, F Xie, H Yan, Y Constantinides, O Oakley, GE Karniadakis
Journal of Fluids and Structures 54, 679-700, 2015
A review of local-to-nonlocal coupling methods in nonlocal diffusion and nonlocal mechanics
M D’Elia, X Li, P Seleson, X Tian, Y Yu
Journal of Peridynamics and Nonlocal Modeling, 1-50, 2021
A physically consistent, flexible, and efficient strategy to convert local boundary conditions into nonlocal volume constraints
M D'Elia, X Tian, Y Yu
SIAM Journal on Scientific Computing 42 (4), A1935-A1949, 2020
Interfacing finite elements with deep neural operators for fast multiscale modeling of mechanics problems
M Yin, E Zhang, Y Yu, GE Karniadakis
Computer methods in applied mechanics and engineering 402, 115027, 2022
Nonlocal kernel network (NKN): a stable and resolution-independent deep neural network
H You, Y Yu, M D'Elia, T Gao, S Silling
Journal of Computational Physics 469, 111536, 2022
Data-driven learning of nonlocal models: from high-fidelity simulations to constitutive laws
H You, Y Yu, S Silling, M D'Elia
AAAI Spring Symposium: MLPS, 2021
Error estimates for projection-based dynamic augmented Lagrangian boundary condition enforcement, with application to fluid–structure interaction
Y Yu, D Kamensky, MC Hsu, XY Lu, Y Bazilevs, TJR Hughes
Mathematical Models and Methods in Applied Sciences 28 (12), 2457-2509, 2018
DAGs with No Curl: An Efficient DAG Structure Learning Approach
Y Yu, T Gao, N Yin, Q Ji
Proceedings of the 38th International Conference on Machine Learning, 2021
DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks
D Wei, T Gao, Y Yu
Advances in Neural Information Processing Systems 33, 2020
Physics-informed deep neural operators networks
S Goswami, A Bora, Y Yu, GE Karniadakis
arXiv preprint arXiv:2207.05748, 2022
A partitioned coupling framework for peridynamics and classical theory: analysis and simulations
Y Yu, FF Bargos, H You, ML Parks, ML Bittencourt, GE Karniadakis
Computer Methods in Applied Mechanics and Engineering 340, 905-931, 2018
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