| 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 | 276 | 2020 |
| An adaptive refinement approach for topology optimization based on separated density field description Y Wang, Z Kang, Q He Computers & Structures 117, 10-22, 2013 | 89 | 2013 |
| Physics‐Informed Neural Network Method for Forward and Backward Advection‐Dispersion Equations QZ He, AM Tartakovsky Water Resources Research 57 (7), e2020WR029479, 2021 | 86 | 2021 |
| 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 | 82 | 2020 |
| Adaptive topology optimization with independent error control for separated displacement and density fields Y Wang, Z Kang, Q He Computers & Structures 135, 50-61, 2014 | 79 | 2014 |
| 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 | 66 | 2014 |
| 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 | 62 | 2021 |
| Manifold learning based data-driven modeling for soft biological tissues Q He, DW Laurence, CH Lee, JS Chen Journal of Biomechanics 117, 110124, 2021 | 43 | 2021 |
| Multi-scale modelling of sandwich structures using hierarchical kinematics QZ He, H Hu, S Belouettar, G Guinta, K Yu, Y Liu, F Biscani, E Carrera, ... Composite structures 93 (9), 2375-2383, 2011 | 37 | 2011 |
| 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 | 33 | 2023 |
| 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 | 30 | 2022 |
| Physics-informed machine learning with conditional Karhunen-Loève expansions AM Tartakovsky, DA Barajas-Solano, Q He Journal of Computational Physics 426, 109904, 2021 | 30 | 2021 |
| 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 | 27 | 2019 |
| Modeling density-driven flow in porous media by physics-informed neural networks for CO2 sequestration H Du, Z Zhao, H Cheng, J Yan, QZ He Computers and Geotechnics 159, 105433, 2023 | 20 | 2023 |
| 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 | 20 | 2022 |
| 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 | 16 | 2023 |
| 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 | 16 | 2021 |
| 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 | 14 | 2023 |
| 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 | 14 | 2022 |
| 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 | 14 | 2020 |
