| Efficient training of physics‐informed neural networks via importance sampling MA Nabian, RJ Gladstone, H Meidani Computer‐Aided Civil and Infrastructure Engineering 36 (8), 962-977, 2021 | 162 | 2021 |
| Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks MA Nabian, H Meidani Computer‐Aided Civil and Infrastructure Engineering 33 (6), 443-458, 2018 | 144 | 2018 |
| A deep learning solution approach for high-dimensional random differential equations MA Nabian, H Meidani Probabilistic Engineering Mechanics 57, 14-25, 2019 | 137* | 2019 |
| Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory X Wu, T Kozlowski, H Meidani, K Shirvan Nuclear Engineering and Design 335, 339-355, 2018 | 109 | 2018 |
| Physics-driven regularization of deep neural networks for enhanced engineering design and analysis MA Nabian, H Meidani Journal of Computing and Information Science in Engineering 20 (1), 011006, 2020 | 92* | 2020 |
| Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data X Wu, T Kozlowski, H Meidani Reliability Engineering & System Safety 169, 422-436, 2018 | 75 | 2018 |
| Gradient based design optimization under uncertainty via stochastic expansion methods V Keshavarzzadeh, H Meidani, DA Tortorelli Computer Methods in Applied Mechanics and Engineering 306, 47-76, 2016 | 66 | 2016 |
| Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE X Wu, T Kozlowski, H Meidani, K Shirvan Nuclear Engineering and Design 335, 417-431, 2018 | 57 | 2018 |
| Predicting Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests MA Nabian, N Alemazkoor, H Meidani Transportation Research Record 2673 (5), 564-573, 2019 | 50 | 2019 |
| Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model X Wu, T Mui, G Hu, H Meidani, T Kozlowski Nuclear Engineering and Design 319, 185-200, 2017 | 38 | 2017 |
| Wavelet approximation of earthquake strong ground motion-goodness of fit for a database in terms of predicting nonlinear structural response MI Todorovska, H Meidani, MD Trifunac Soil Dynamics and Earthquake Engineering 29 (4), 742-751, 2009 | 36 | 2009 |
| Divide and conquer: An incremental sparsity promoting compressive sampling approach for polynomial chaos expansions N Alemazkoor, H Meidani Computer Methods in Applied Mechanics and Engineering 318, 937-956, 2017 | 30 | 2017 |
| Multiscale Markov models with random transitions for energy demand management H Meidani, R Ghanem Energy and Buildings 61, 267-274, 2013 | 30 | 2013 |
| A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions N Alemazkoor, H Meidani Journal of Computational Physics 371, 137-151, 2018 | 28 | 2018 |
| Survival analysis at multiple scales for the modeling of track geometry deterioration N Alemazkoor, CJ Ruppert, H Meidani Proceedings of the Institution of Mechanical Engineers, Part F: Journal of …, 2018 | 24 | 2018 |
| PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations W Zhong, H Meidani Computer Methods in Applied Mechanics and Engineering 403, 115664, 2023 | 21 | 2023 |
| Random Markov decision processes for sustainable infrastructure systems H Meidani, R Ghanem Structure and Infrastructure Engineering 11 (5), 655-667, 2015 | 21 | 2015 |
| IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data A Kazemi, H Meidani IEEE Access 9, 112966-112977, 2021 | 17* | 2021 |
| Spectral power iterations for the random eigenvalue problem H Meidani, R Ghanem AIAA journal 52 (5), 912-925, 2014 | 16 | 2014 |
| Mesh-based GNN surrogates for time-independent PDEs RJ Gladstone, H Rahmani, V Suryakumar, H Meidani, M D’Elia, A Zareei Scientific Reports 14 (1), 3394, 2024 | 14* | 2024 |
