フォロー
Negin Alemazkoor
Negin Alemazkoor
Assistant Professor, University of Virginia
確認したメール アドレス: virginia.edu - ホームページ
タイトル
引用先
引用先
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
542019
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
362020
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
352017
A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions
N Alemazkoor, H Meidani
Journal of Computational Physics 371, 137-151, 2018
302018
The impact of HOT lanes on carpools
M Burris, N Alemazkoor, R Benz, NS Wood
Research in Transportation Economics 44, 43-51, 2014
302014
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
282018
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
252022
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
172015
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
132018
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
112022
Fast Probabilistic Voltage control for distribution networks with distributed generation using polynomial surrogates
N Alemazkoor, H Meidani
IEEE Access 8, 73536-73546, 2020
102020
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
82024
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
72024
Examining Impacts of Increasing Speed Limit on Speed Distribution: Case Study
N Alemazkoor, H Hawkins
Transportation Research Board 93rd Annual MeetingTransportation Research Board, 2014
72014
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
62024
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
52024
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
52021
Efficient collection of connected vehicles data with precision guarantees
N Alemazkoor, H Meidani
IEEE Transactions on Intelligent Transportation Systems 21 (11), 4637-4645, 2019
52019
Examining potential travel time savings benefits due to toll rates that vary by lane
N Alemazkoor, M Burris
Journal of Transportation Technologies 2014, 2014
52014
HEvOD: a database of hurricane evacuation orders in the United States
H Anand, N Alemazkoor, M Shafiee-Jood
Scientific data 11 (1), 270, 2024
42024
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