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h5 指数:97
h5 中位数:133

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

标题/作者	引用次数	年份
The Arithmetic Optimization Algorithm L Abualigah, A Diabat, S Mirjalili, M Abd Elaziz, AH Gandomi Computer Methods in Applied Mechanics and Engineering 376, 113609	1294	2021
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: Concepts, implementation and applications E Samaniego, C Anitescu, S Goswami, VM Nguyen-Thanh, H Guo, ... Computer Methods in Applied Mechanics and Engineering 362, 112790	734	2020
Physics-informed neural networks for high-speed flows Z Mao, AD Jagtap, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 360, 112789	525	2020
New computational approach for exergy and entropy analysis of nanofluid under the impact of Lorentz force through a porous media M Sheikholeslami Computer Methods in Applied Mechanics and Engineering 344, 319-333	507	2019
Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data L Sun, H Gao, S Pan, JX Wang Computer Methods in Applied Mechanics and Engineering 361, 112732	465	2020
Numerical approach for MHD Al2O3-water nanofluid transportation inside a permeable medium using innovative computer method M Sheikholeslami Computer Methods in Applied Mechanics and Engineering 344, 306-318	455	2019
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems AD Jagtap, E Kharazmi, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 365, 113028	375	2020
A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics E Haghighat, M Raissi, A Moure, H Gomez, R Juanes Computer Methods in Applied Mechanics and Engineering 379, 113741	313	2021
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	310	2020
Dwarf Mongoose Optimization Algorithm JO Agushaka, AE Ezugwu, L Abualigah Computer Methods in Applied Mechanics and Engineering 391, 114570	277	2022
hp-VPINNs: Variational physics-informed neural networks with domain decomposition E Kharazmi, Z Zhang, GEM Karniadakis Computer Methods in Applied Mechanics and Engineering 374, 113547	265	2021
PPINN: Parareal physics-informed neural network for time-dependent PDEs X Meng, Z Li, D Zhang, GE Karniadakis Computer Methods in Applied Mechanics and Engineering 370, 113250	262	2020
A multiscale multi-permeability poroplasticity model linked by recursive homogenizations and deep learning K Wang, WC Sun Computer Methods in Applied Mechanics and Engineering 334, 337-380	247	2018
A phase field formulation for hydrogen assisted cracking E Martínez-Pañeda, A Golahmar, CF Niordson Computer Methods in Applied Mechanics and Engineering 342, 742-761	246	2018
Application of Neural Network for estimation of heat transfer treatment of Al2O3-H2O nanofluid through a channel M Sheikholeslami, MB Gerdroodbary, R Moradi, A Shafee, Z Li Computer Methods in Applied Mechanics and Engineering 344, 1-12	243	2019
A multi-material level set-based topology optimization of flexoelectric composites H Ghasemi, HS Park, T Rabczuk Computer Methods in Applied Mechanics and Engineering 332, 47-62	232	2018
Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications W Zhao, L Wang, S Mirjalili Computer Methods in Applied Mechanics and Engineering 388, 114194	231	2022
Functionally graded lattice structure topology optimization for the design of additive manufactured components with stress constraints L Cheng, J Bai, AC To Computer Methods in Applied Mechanics and Engineering 344, 334-359	217	2019
SciANN: A Keras/TensorFlow wrapper for scientific computations and physics-informed deep learning using artificial neural networks E Haghighat, R Juanes Computer Methods in Applied Mechanics and Engineering 373, 113552	204	2021
Predicting the effective mechanical property of heterogeneous materials by image based modeling and deep learning X Li, Z Liu, S Cui, C Luo, C Li, Z Zhuang Computer Methods in Applied Mechanics and Engineering 347, 735-753	199	2019

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