Yibo Yang
Cited by
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Adversarial uncertainty quantification in physics-informed neural networks
Y Yang, P Perdikaris
Journal of Computational Physics 394, 136-152, 2019
Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks
G Kissas, Y Yang, E Hwuang, WR Witschey, JA Detre, P Perdikaris
Computer Methods in Applied Mechanics and Engineering 358, 112623, 2020
Physics-informed neural networks for cardiac activation mapping
F Sahli Costabal, Y Yang, P Perdikaris, DE Hurtado, E Kuhl
Frontiers in Physics 8, 42, 2020
Physics-informed deep generative models
Y Yang, P Perdikaris
Third workshop on Bayesian Deep Learning (NeurIPS 2018), Montréal, Canada., 2018
Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems
Y Yang, P Perdikaris
Computational Mechanics 64 (2), 417–434, 2019
Accurate artificial boundary conditions for the semi-discretized linear Schrödinger and heat equations on rectangular domains
S Ji, Y Yang, G Pang, X Antoine
Computer Physics Communications 222, 84-93, 2018
Differential operator multiplication method for fractional differential equations
S Tang, Y Ying, Y Lian, S Lin, Y Yang, GJ Wagner, WK Liu
Computational Mechanics 58 (5), 879-888, 2016
Bayesian differential programming for robust systems identification under uncertainty
Y Yang, M Aziz Bhouri, P Perdikaris
Proceedings of the Royal Society A 476 (2243), 20200290, 2020
Exact boundary condition for semi-discretized Schrödinger equation and heat equation in a rectangular domain
G Pang, Y Yang, S Tang
Journal of Scientific Computing 72 (1), 1-13, 2017
Eliminating corner effects in square lattice simulation
G Pang, S Ji, Y Yang, S Tang
Computational Mechanics 62 (1), 111-122, 2018
Stability and convergence analysis of artificial boundary conditions for the Schrödinger equation on a rectangular domain
G Pang, Y Yang, X Antoine, S Tang
Mathematics of Computation 90 (332), 26, 2021
Accurate artificial boundary conditions for semi-discretized one-dimensional peridynamics
S Ji, G Pang, J Zhang, Y Yang, P Perdikaris
Proceedings of the Royal Society A 477 (2250), 20210229, 2021
Physics-Informed Deep Generative Models for Scalable Uncertainty Quantification
Y Yang, P Perdikaris
Output-Weighted Sampling for Multi-Armed Bandits with Extreme Payoffs
Y Yang, A Blanchard, T Sapsis, P Perdikaris
arXiv preprint arXiv:2102.10085, 2021
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