| NVIDIA SimNet™: An AI-accelerated multi-physics simulation framework O Hennigh, S Narasimhan, MA Nabian, A Subramaniam, K Tangsali, ... International Conference on Computational Science, 447-461, 2021 | 255* | 2021 |
| Deep physical informed neural networks for metamaterial design Z Fang, J Zhan Ieee Access 8, 24506-24513, 2019 | 139 | 2019 |
| A high-efficient hybrid physics-informed neural networks based on convolutional neural network Z Fang IEEE Transactions on Neural Networks and Learning Systems 33 (10), 5514-5526, 2021 | 110 | 2021 |
| A physics-informed neural network framework for PDEs on 3D surfaces: Time independent problems Z Fang, J Zhan IEEE Access 8, 26328-26335, 2019 | 59 | 2019 |
| A superconvergent fitted finite volume method for B lack–S choles equations governing E uropean and A merican option valuation S Wang, S Zhang, Z Fang Numerical Methods for Partial Differential Equations 31 (4), 1190-1208, 2015 | 47 | 2015 |
| A new FDTD scheme for Maxwell’s equations in Kerr-type nonlinear media H Jia, J Li, Z Fang, M Li Numerical Algorithms 82, 223-243, 2019 | 18 | 2019 |
| Regularity analysis of metamaterial Maxwell’s equations with random coefficients and initial conditions J Li, Z Fang, G Lin Computer Methods in Applied Mechanics and Engineering 335, 24-51, 2018 | 16 | 2018 |
| Efficient stochastic Galerkin methods for Maxwell’s equations with random inputs Z Fang, J Li, T Tang, T Zhou Journal of Scientific Computing 80, 248-267, 2019 | 14 | 2019 |
| Development and analysis of Crank‐Nicolson scheme for metamaterial Maxwell's equations on nonuniform rectangular grids X Wang, J Li, Z Fang Numerical Methods for Partial Differential Equations 34 (6), 2040-2059, 2018 | 12 | 2018 |
| Mathematical analysis of Ziolkowski’s PML model with application for wave propagation in metamaterials Y Huang, J Li, Z Fang Journal of Computational and Applied Mathematics 366, 112434, 2020 | 10 | 2020 |
| Optimal control for electromagnetic cloaking metamaterial parameters design Z Fang, J Li, X Wang Computers & Mathematics with Applications 79 (4), 1165-1176, 2020 | 9 | 2020 |
| A physics-informed neural network framework for partial differential equations on 3d surfaces: Time-dependent problems Z Fang, J Zhang, X Yang arXiv preprint arXiv:2103.13878, 2021 | 7 | 2021 |
| Ensemble learning for physics informed neural networks: A gradient boosting approach Z Fang, S Wang, P Perdikaris ICLR 2024, 2024 | 6 | 2024 |
| Learning only on boundaries: a physics-informed neural operator for solving parametric partial differential equations in complex geometries Z Fang, S Wang, P Perdikaris Neural Computation 36 (3), 475-498, 2024 | 5 | 2024 |
| Analysis and application of stochastic collocation methods for Maxwell’s equations with random inputs J Li, Z Fang Adv. Appl. Math. Mech 10 (6), 1305-1326, 2018 | 4 | 2018 |
| A note on breaking of symmetry for a class of variational problems DG Costa, Z Fang Applied Mathematics Letters 98, 329-335, 2019 | 2 | 2019 |
| A fitted finite volume method for unit-linked policy with surrender option S Chang, Z Fang, X Liu, V Shaydurov Comput. Res 2, 49-53, 2014 | 1 | 2014 |
| SimNet: A Neural Framework for Physics Simulations O Hennigh, M Nabian, A Subramaniam, K Tangsali, Z Fang, S Wang, ... APS Annual Gaseous Electronics Meeting Abstracts, BM22. 005, 2021 | | 2021 |
| Analysis and Application of Single Level, Multi-Level Monte Carlo and Quasi-Monte Carlo Finite Element Methods for Time-Dependent Maxwell’s Equations with Random Inputs X Wang, J Li, Z Fang Communications in Computational Physics 29 (1), 211-236, 2021 | | 2021 |
| Uncertainty Quantification for Maxwell's Equations Z Fang University of Nevada, Las Vegas, 2020 | | 2020 |
