热门出版物 - 学术资源搜索

h5 指数:80
h5 中位数:118

h5 指数是指在过去整整 5 年中所发表文章的 h 指数。h 指在 2018-2022 年间发表的 h 篇文章每篇至少都被引用过 h 次的最大值。隐藏

出版物的 h5 中位数，是指出版物的 h5 指数所涵盖的所有文章获得的引用次数的中位值。隐藏

#2 Computational Mathematics

标题/作者	引用次数	年份
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations M Raissi, P Perdikaris, GE Karniadakis Journal of Computational Physics 378, 686-707	5785	2019
DGM: A deep learning algorithm for solving partial differential equations J Sirignano, K Spiliopoulos Journal of Computational Physics 375, 1339-1364	1557	2018
Hidden physics models: Machine learning of nonlinear partial differential equations M Raissi, GE Karniadakis Journal of Computational Physics 357, 125-141	1005	2018
Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data Y Zhu, N Zabaras, PS Koutsourelakis, P Perdikaris Journal of Computational Physics 394, 56-81	673	2019
The scalar auxiliary variable (SAV) approach for gradient flows J Shen, J Xu, J Yang Journal of Computational Physics 353, 407-416	584	2018
Bayesian deep convolutional encoder–decoder networks for surrogate modeling and uncertainty quantification Y Zhu, N Zabaras Journal of Computational Physics 366, 415-447	514	2018
Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders K Lee, KT Carlberg Journal of Computational Physics 404, 108973	505	2020
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations X Jin, S Cai, H Li, GE Karniadakis Journal of Computational Physics 426, 109951	452	2021
PDE-Net 2.0: Learning PDEs from data with a numeric-symbolic hybrid deep network Z Long, Y Lu, B Dong Journal of Computational Physics 399, 108925	419	2019
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data L Yang, X Meng, GE Karniadakis Journal of Computational Physics 425, 109913	415	2021
Adaptive activation functions accelerate convergence in deep and physics-informed neural networks AD Jagtap, K Kawaguchi, GE Karniadakis Journal of Computational Physics 404, 109136	408	2020
When and why PINNs fail to train: A neural tangent kernel perspective S Wang, X Yu, P Perdikaris Journal of Computational Physics 449, 110768	390	2022
Non-intrusive reduced order modeling of nonlinear problems using neural networks JS Hesthaven, S Ubbiali Journal of Computational Physics 363, 55-78	382	2018
Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification RK Tripathy, I Bilionis Journal of Computational Physics 375, 565-588	378	2018
A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems X Meng, GE Karniadakis Journal of Computational Physics 401, 109020	344	2020
A review of level-set methods and some recent applications F Gibou, R Fedkiw, S Osher Journal of Computational Physics 353, 82-109	342	2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems D Zhang, L Lu, L Guo, GE Karniadakis Journal of Computational Physics 397, 108850	308	2019
Adversarial uncertainty quantification in physics-informed neural networks Y Yang, P Perdikaris Journal of Computational Physics 394, 136-152	294	2019
A review of definitions of fractional derivatives and other operators GS Teodoro, JAT Machado, EC de Oliveira Journal of Computational Physics 388, 195-208	279	2019
What is the fractional Laplacian? A comparative review with new results A Lischke, G Pang, M Gulian, F Song, C Glusa, X Zheng, Z Mao, W Cai, ... Journal of Computational Physics 404, 109009	258	2020

1 - 20

计算机程序会自动估算并确定日期和引用次数。