Selected Journal Publications

18. M. Nabian and H. Meidani (2018). Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis, Journal of Computing and Information Science in Engineering 20 (1). link

17. N. Alemazkoor and H. Meidani (2018). Efficient Collection of Connected Vehicles Data with Precision Guarantees, IEEE Transactions on Intelligent Transportation Systems, in press. link

16. M. Nabian and H. Meidani (2018). A deep learning solution approach for high-dimensional random differential equations, Probabilistic Engineering Mechanics 57, 14-25. link

15. N. Alemazkoor and H. Meidani (2018). A Near-Optimal Sampling Strategy for Sparse Recovery of Polynomial Chaos Expansions, Journal of Computational Physics 371, 137-151. link

14. N. Alemazkoor and H. Meidani (2018). A preconditioning approach for improved estimation of sparse polynomial chaos expansions. link

13. M. Nabian, H. Meidani (2017). Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks. Computer‐Aided Civil and Infrastructure Engineering 33 (6), 443-458. link

12. X Wu, T Kozlowski, H Meidani, K Shirvan (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 1: Theory. NED 335, 339-355.

11. X Wu, T Kozlowski, H Meidani, K Shirvan (2018). Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE. NED 335, 417-431.

10. N. Alemazkoor and H. Meidani (2017). Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions,  Computer Methods in Applied Mechanics and Engineering, 318, pp. 937-956. link

9. X. Wu, T. Mui, G. Hu, H. Meidani, T. Kozlowski (2017). Inverse Uncertainty Quantification of TRACE Physical Model Parameters Using Sparse Grid Stochastic Collocation Surrogate Model, NED, 319, 185-200.

8. N. Alemazkoor, C.J. Ruppert and H. Meidani (2018). Survival Analysis at Multiple Scales for Track Geometry Deterioration Modeling, Journal of Rail and Rapid Transit, Proceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit, 232(3), 842-850.

7. V. Keshavarzzadeh, H. Meidani and D.A. Tortorelli (2016). Gradient Based Design Optimization under Uncertainty via Stochastic Expansion Methods,  Computer Methods in Applied Mechanics and Engineering, 306, pp. 47–76.  link

6. H. Meidani, J.B. Hooper, R.M. Kirby and D. Bedrov (2015). Calibration and Ranking of Coarse-grained Water Models using the Bayesian Formalism, International Journal for Uncertainty Quantification, 7(2). link

5. H. Meidani and R. Ghanem (2015). Random Markov Decision Processes for Sustainable Infrastructure Systems, Structure and Infrastructure Engineering, Maintenance, Management, Life-Cycle Design and Performance, 11(5), pp. 655-667. link

4. H. Meidani and R. Ghanem (2014). Multiscale Markov Models with Random Transitions for Energy Demand Management, Energy and Buildings, 61, pp 267-274. link

3. H. Meidani and R. Ghanem (2014). Spectral Power Iterations for the Random Eigenvalue Problem, AIAA Journal, 52(5), pp. 912-925. link

2. H. Meidani and R. Ghanem (2012). Uncertainty Quantification for Markov Chain Models, Chaos: An Interdisciplinary Journal of Nonlinear Science, 22(4) link

1. M. Todorovska , H. Meidani and M. Trifunac (2009). Wavelet Approximation of Earthquake Strong Ground Motion – Goodness of Fit for a Database in Terms of Predicting Nonlinear Structural Response, Soil Dynamics and Earthquake Engineering , 29(4), pp 742-751, link