Negin Alemazkoor

Orcid: 0000-0003-0221-3985

According to our database1, Negin Alemazkoor authored at least 14 papers between 2018 and 2026.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes.
CoRR, April, 2026

2025
Multifidelity graph neural networks for efficient and accurate mesh-based partial differential equations surrogate modeling.
Comput. Aided Civ. Infrastructure Eng., March, 2025

Multi-fidelity graph neural networks for efficient and accurate flood hazard mapping.
Environ. Model. Softw., 2025

Interpretable physics-informed graph neural networks for flood forecasting.
Comput. Aided Civ. Infrastructure Eng., 2025

2024
Multi-Fidelity Physics-Informed Generative Adversarial Network for Solving Partial Differential Equations.
J. Comput. Inf. Sci. Eng., 2024

Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization.
CoRR, 2024

Graph Neural Networks for Precision-Guaranteed Compression of Large-scale Data.
Proceedings of the IEEE International Conference on Big Data, 2024

2023
Perspicuity of Evacuation Behavior in Communities during Hurricanes using Large-scale Mobility Patterns and Communal Characteristics.
Proceedings of the 57th Annual Conference on Information Sciences and Systems, 2023

2022
Smart-Meter Big Data for Load Forecasting: An Alternative Approach to Clustering.
IEEE Access, 2022

2021
A Data-Driven Multi-Fidelity Approach for Traffic State Estimation Using Data From Multiple Sources.
IEEE Access, 2021

2020
Efficient Collection of Connected Vehicles Data With Precision Guarantees.
IEEE Trans. Intell. Transp. Syst., 2020

Fast Probabilistic Voltage Control for Distribution Networks With Distributed Generation Using Polynomial Surrogates.
IEEE Access, 2020

2018
A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions.
J. Comput. Phys., 2018

A Recursive Data-driven Model for Traffic Flow Predictions for Locations with Faulty Sensors.
Proceedings of the 21st International Conference on Intelligent Transportation Systems, 2018


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