Artículos con órdenes de acceso público - QiZhi HeMás información
Disponibles en algún lugar: 19
Physics-informed neural networks for multiphysics data assimilation with application to subsurface transport
QZ He, D Barajas-Solano, G Tartakovsky, AM Tartakovsky
Advances in Water Resources 141, 103610, 2020
Órdenes: US Department of Energy
Physics‐Informed Neural Network Method for Forward and Backward Advection‐Dispersion Equations
QZ He, AM Tartakovsky
Water Resources Research 57 (7), e2020WR029479, 2021
Órdenes: US Department of Energy
A physics-constrained data-driven approach based on locally convex reconstruction for noisy database
Q He, JS Chen
Computer Methods in Applied Mechanics and Engineering 363, 112791, 2020
Órdenes: US National Science Foundation
Deep autoencoders for physics-constrained data-driven nonlinear materials modeling
X He, Q He, JS Chen
Computer Methods in Applied Mechanics and Engineering 385, 114034, 2021
Órdenes: US National Science Foundation, US Department of Energy
A topology optimization method for geometrically nonlinear structures with meshless analysis and independent density field interpolation
Q He, Z Kang, Y Wang
Computational Mechanics 54, 629-644, 2014
Órdenes: National Natural Science Foundation of China
Physics‐Informed Neural Networks of the Saint‐Venant Equations for Downscaling a Large‐Scale River Model
D Feng, Z Tan, QZ He
Water Resources Research 59 (2), e2022WR033168, 2023
Órdenes: US Department of Energy
Manifold learning based data-driven modeling for soft biological tissues
Q He, DW Laurence, CH Lee, JS Chen
Journal of Biomechanics 117, 110124, 2021
Órdenes: US National Science Foundation, US National Institutes of Health, American …
Physics-constrained deep neural network method for estimating parameters in a redox flow battery
QZ He, P Stinis, AM Tartakovsky
Journal of Power Sources 528, 231147, 2022
Órdenes: US Department of Energy
Physics-informed machine learning with conditional Karhunen-Loève expansions
AM Tartakovsky, DA Barajas-Solano, Q He
Journal of Computational Physics 426, 109904, 2021
Órdenes: US Department of Energy
Microstructural analysis of skeletal muscle force generation during aging
Y Zhang, JS Chen, Q He, X He, RR Basava, J Hodgson, U Sinha, S Sinha
International Journal for Numerical Methods in Biomedical Engineering, 2019
Órdenes: US National Institutes of Health
A Feature-Encoded Physics-Informed Parameter Identification Neural Network for Musculoskeletal Systems
K Taneja, X He, Q He, X Zhao, YA Lin, KJ Loh, JS Chen
Journal of Biomechanical Engineering 144 (12), 121006, 2022
Órdenes: US Department of Defense, US National Institutes of Health
A hybrid deep neural operator/finite element method for ice-sheet modeling
QZ He, M Perego, AA Howard, GE Karniadakis, P Stinis
Journal of Computational Physics 492, 112428, 2023
Órdenes: US Department of Energy
Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions
Y Zong, QZ He, AM Tartakovsky
Computer Methods in Applied Mechanics and Engineering 414, 116125, 2023
Órdenes: US Department of Energy
A hyper-reduction computational method for accelerated modeling of thermal cycling-induced plastic deformations
S Kaneko, H Wei, Q He, JS Chen, S Yoshimura
Journal of the Mechanics and Physics of Solids 151, 104385, 2021
Órdenes: US National Science Foundation
Enhanced physics-constrained deep neural networks for modeling vanadium redox flow battery
QZ He, Y Fu, P Stinis, A Tartakovsky
Journal of Power Sources, 2022
Órdenes: US Department of Energy
Physics-constrained local convexity data-driven modeling of anisotropic nonlinear elastic solids
X He, Q He, JS Chen, U Sinha, S Sinha
Data-Centric Engineering 1, e19, 2020
Órdenes: US National Science Foundation, US National Institutes of Health
A decomposed subspace reduction for fracture mechanics based on the meshfree integrated singular basis function method
QZ He, JS Chen, C Marodon
Computational Mechanics, 2018
Órdenes: US Department of Defense
Modeling land ice with deep operator networks.
Q He, M Perego
Sandia National Lab.(SNL-NM), Albuquerque, NM (United States), 2021
Órdenes: US Department of Energy
Represent precipitation-induced geological hazards in Earth system models using artificial intelligence
Z Tan, Q He, Q Zhu, C Liao
Pacific Northwest National Lab.(PNNL), Richland, WA (United States …, 2021
Órdenes: US Department of Energy
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