The generalization of Latin hypercube sampling MD Shields, J Zhang Reliability Engineering & System Safety 148, 96-108, 2016 | 418 | 2016 |
Benchmarking graph neural networks for materials chemistry V Fung, J Zhang, E Juarez, B Sumpter npj Computational Materials, 2021 | 179 | 2021 |
Machine learning for high-entropy alloys: Progress, challenges and opportunities X Liu, J Zhang, Z Pei Progress in Materials Science 131, 101018, 2023 | 107 | 2023 |
Simulation intelligence: Towards a new generation of scientific methods A Lavin, D Krakauer, H Zenil, J Gottschlich, T Mattson, J Brehmer, ... arXiv preprint arXiv:2112.03235, 2021 | 107 | 2021 |
On the quantification and efficient propagation of imprecise probabilities resulting from small datasets J Zhang, MD Shields Mechanical Systems and Signal Processing 98, 465-483, 2018 | 97 | 2018 |
Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Survey J Zhang Wiley Interdisciplinary Reviews: Computational Statistics, 2020 | 93 | 2020 |
Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage Z Li, J Zhang, L Liu, J Liu CVPR 2022, 2022 | 91 | 2022 |
Robust data-driven approach for predicting the configurational energy of high entropy alloys J Zhang, X Liu, S Bi, J Yin, G Zhang, M Eisenbach Materials & Design 185, 108247, 2020 | 62 | 2020 |
Transfer learning based variable-fidelity surrogate model for shell buckling prediction K Tian, Z Li, J Zhang, L Huang, B Wang Composite Structures 273, 114285, 2021 | 58 | 2021 |
Optimum design of hierarchical stiffened shells for low imperfection sensitivity B Wang, P Hao, G Li, JX Zhang, KF Du, K Tian, XJ Wang, XH Tang Acta Mechanica Sinica 30 (3), 391-402, 2014 | 56 | 2014 |
Monte Carlo simulation of order-disorder transition in refractory high entropy alloys: a data-driven approach X Liu, J Zhang, J Yin, S Bi, M Eisenbach, Y Wang Computational Materials Science 187, 110135, 2021 | 50 | 2021 |
Learning nonlinear level sets for dimensionality reduction in function approximation G Zhang, J Zhang, J Hinkle NeurIPS 2019, 2019 | 46* | 2019 |
Tailoring the optimal load-carrying efficiency of hierarchical stiffened shells by competitive sampling K Tian, B Wang, K Zhang, J Zhang, P Hao, Y Wu Thin-Walled Structures 133, 216-225, 2018 | 42 | 2018 |
The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets J Zhang, MD Shields Computer Methods in Applied Mechanics and Engineering 334 (1), 483-506, 2018 | 39 | 2018 |
Design Optimization of Connection Section for Concentrated Force Diffusion J Zhang, B Wang, F Niu, G Cheng Mechanics Based Design of Structures and Machines: An International Journal, 2015 | 29 | 2015 |
Inverse design of two-dimensional materials with invertible neural networks V Fung, J Zhang, G Hu, P Ganesh, BG Sumpter npj Computational Materials 7 (1), 200, 2021 | 27 | 2021 |
A directional Gaussian smoothing optimization method for computational inverse design in nanophotonics J Zhang, S Bi, G Zhang Materials & Design, 2021 | 26 | 2021 |
Toward the robust establishment of variable-fidelity surrogate models for hierarchical stiffened shells by two-step adaptive updating approach K Tian, Z Li, X Ma, H Zhao, J Zhang, B Wang Structural and Multidisciplinary Optimization 61, 1515-1528, 2020 | 25 | 2020 |
Efficient Monte Carlo resampling for probability measure changes from Bayesian updating J Zhang, MD Shields Probabilistic Engineering Mechanics 55, 54-66, 2019 | 24* | 2019 |
Imprecise global sensitivity analysis using Bayesian multimodel inference and importance sampling J Zhang, S TerMaath, MD Shields Mechanical Systems and Signal Processing 148, 107162, 2021 | 23 | 2021 |