Hadi Meidani, Ph.D.
Affiliations: | 2012 | Civil Engineering | University of Southern California, Los Angeles, CA, United States |
Area:
Civil Engineering, Electronics and Electrical Engineering, Mechanical EngineeringGoogle:
"Hadi Meidani"Parents
Sign in to add mentor| Roger G. Ghanem | grad student | 2012 | USC | |
| (Uncertainty management for complex systems of systems: Applications to the future smart grid.) | ||||
BETA: Related publications
See more...
Publications
|
You can help our author matching system! If you notice any publications incorrectly attributed to this author, please sign in and mark matches as correct or incorrect. |
| Nabian MA, Meidani H. (2020) Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis Journal of Computing and Information Science in Engineering. 20 |
| Alemazkoor N, Meidani H. (2020) Fast Probabilistic Voltage Control for Distribution Networks With Distributed Generation Using Polynomial Surrogates Ieee Access. 8: 73536-73546 |
| Alemazkoor N, Meidani H. (2019) Efficient Collection of Connected Vehicles Data With Precision Guarantees Ieee Transactions On Intelligent Transportation Systems. 1-9 |
| Gungor OE, Petit AMA, Qiu J, et al. (2019) Development of an overweight vehicle permit fee structure for Illinois Transport Policy. 82: 26-35 |
| Nabian MA, Meidani H. (2019) A deep learning solution approach for high-dimensional random differential equations Probabilistic Engineering Mechanics. 57: 14-25 |
| Wu X, Kozlowski T, Meidani H. (2018) Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data Reliability Engineering & System Safety. 169: 422-436 |
| Wu X, Kozlowski T, Meidani H, et al. (2018) Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 1: Theory Nuclear Engineering and Design. 335: 339-355 |
| Wu X, Kozlowski T, Meidani H, et al. (2018) Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE Nuclear Engineering and Design. 335: 417-431 |
| Alemazkoor N, Meidani H. (2018) A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions Journal of Computational Physics. 371: 137-151 |
| Wu X, Mui T, Hu G, et al. (2017) Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model Nuclear Engineering and Design. 319: 185-200 |