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 | 54 | 2019 |
Hurricane-induced power outage risk under climate change is primarily driven by the uncertainty in projections of future hurricane frequency N Alemazkoor, B Rachunok, DR Chavas, A Staid, A Louhghalam, ... Scientific reports 10 (1), 15270, 2020 | 36 | 2020 |
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 | 35 | 2017 |
A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions N Alemazkoor, H Meidani Journal of Computational Physics 371, 137-151, 2018 | 30 | 2018 |
The impact of HOT lanes on carpools M Burris, N Alemazkoor, R Benz, NS Wood Research in Transportation Economics 44, 43-51, 2014 | 30 | 2014 |
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 | 28 | 2018 |
Smart-meter big data for load forecasting: An alternative approach to clustering N Alemazkoor, M Tootkaboni, R Nateghi, A Louhghalam IEEE access 10, 8377-8387, 2022 | 25 | 2022 |
Using empirical data to find the best measure of travel time reliability N Alemazkoor, MW Burris, SR Danda Transportation Research Record 2530 (1), 93-100, 2015 | 17 | 2015 |
A preconditioning approach for improved estimation of sparse polynomial chaos expansions N Alemazkoor, H Meidani Computer Methods in Applied Mechanics and Engineering 342, 474-489, 2018 | 13 | 2018 |
A multi-fidelity polynomial chaos-greedy Kaczmarz approach for resource-efficient uncertainty quantification on limited budget N Alemazkoor, A Louhghalam, M Tootkaboni Computer Methods in Applied Mechanics and Engineering 389, 114290, 2022 | 11 | 2022 |
Fast Probabilistic Voltage control for distribution networks with distributed generation using polynomial surrogates N Alemazkoor, H Meidani IEEE Access 8, 73536-73546, 2020 | 10 | 2020 |
Multi-fidelity graph neural networks for efficient power flow analysis under high-dimensional demand and renewable generation uncertainty M Taghizadeh, K Khayambashi, MA Hasnat, N Alemazkoor Electric Power Systems Research 237, 111014, 2024 | 8 | 2024 |
Multi-fidelity physics-informed generative adversarial network for solving partial differential equations M Taghizadeh, MA Nabian, N Alemazkoor Journal of Computing and Information Science in Engineering 24 (11), 111003, 2024 | 7 | 2024 |
Examining Impacts of Increasing Speed Limit on Speed Distribution: Case Study N Alemazkoor, H Hawkins Transportation Research Board 93rd Annual MeetingTransportation Research Board, 2014 | 7 | 2014 |
Improving accuracy and computational efficiency of optimal design of experiments via greedy backward approach M Taghizadeh, D Xiu, N Alemazkoor International Journal for Uncertainty Quantification 14 (1), 2024 | 6 | 2024 |
Multifidelity graph neural networks for efficient and accurate mesh‐based partial differential equations surrogate modeling M Taghizadeh, MA Nabian, N Alemazkoor Computer‐Aided Civil and Infrastructure Engineering, 2024 | 5 | 2024 |
A data-driven multi-fidelity approach for traffic state estimation using data from multiple sources N Alemazkoor, H Meidani IEEE Access 9, 78128-78137, 2021 | 5 | 2021 |
Efficient collection of connected vehicles data with precision guarantees N Alemazkoor, H Meidani IEEE Transactions on Intelligent Transportation Systems 21 (11), 4637-4645, 2019 | 5 | 2019 |
Examining potential travel time savings benefits due to toll rates that vary by lane N Alemazkoor, M Burris Journal of Transportation Technologies 2014, 2014 | 5 | 2014 |
HEvOD: a database of hurricane evacuation orders in the United States H Anand, N Alemazkoor, M Shafiee-Jood Scientific data 11 (1), 270, 2024 | 4 | 2024 |