Publications
This page follows the current academic CV and includes both peer-reviewed publications and under-review manuscripts. The publications span scientific machine learning, graph-based modeling, physics-informed AI, uncertainty-aware infrastructure analytics, and scalable surrogate modeling for real-world systems.
Peer-Reviewed Publications
Published in Reliability Engineering & System Safety, 2025
A published transportation-resilience paper modeling seismic risk, recovery, and community loss through integrated probabilistic simulation.
Recommended citation: Taghizadeh, M., & Mahsuli, M. (2025). "Probabilistic Modeling of the Risk and Recovery of the Transportation Systems for Community Resilience Analysis." Reliability Engineering & System Safety.
Published in Journal of Hydrology, 2025
A published flood-forecasting paper combining geometry-informed neural operators and domain adaptation for transferable, real-time prediction.
Recommended citation: Taghizadeh, M., Zandsalimi, Z., Nabian, M. A., Goodall, J. L., & Alemazkoor, N. (2025). "FloodForecaster: A Domain-Adaptive Geometry-Informed Neural Operator Framework for Rapid Flood Forecasting." Journal of Hydrology.
Published in Environmental Modelling & Software, 2025
A published multi-fidelity graph neural network framework for scalable flood hazard mapping on unstructured meshes with reduced simulation cost.
Recommended citation: Taghizadeh, M., Zandsalimi, Z., Shafiee-Jood, M., & Alemazkoor, N. (2025). "Multi-Fidelity Graph Neural Networks for Efficient and Accurate Flood Hazard Mapping." Environmental Modelling & Software.
Published in Computer-Aided Civil and Infrastructure Engineering, 2025
A physics-informed flood forecasting paper that improves interpretability, mass conservation, and long-horizon stability on unstructured flood domains.
Recommended citation: Taghizadeh, M., Zandsalimi, Z., Nabian, M. A., Shafiee-Jood, M., & Alemazkoor, N. (2025). "Interpretable Physics-Informed Graph Neural Networks for Flood Forecasting." Computer-Aided Civil and Infrastructure Engineering.
Published in ASME Journal of Computing and Information Science in Engineering, 2024
A multi-fidelity physics-informed generative approach for PDE solving that combines learned surrogates with governing-physics signals.
Recommended citation: Taghizadeh, M., Nabian, M. A., & Alemazkoor, N. (2024). "Multi-fidelity Physics-informed Generative Adversarial Network for Solving Partial Differential Equations." ASME Journal of Computing and Information Science in Engineering.
Published in Electric Power Systems Research, 2024
A graph-based surrogate modeling paper for faster probabilistic power flow analysis under uncertain demand and renewable generation.
Recommended citation: Taghizadeh, M., Khayambashi, K., Hasnat, M. A., & Alemazkoor, N. (2024). "Multi-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty." Electric Power Systems Research.
Published in Computer-Aided Civil and Infrastructure Engineering, 2024
Multi-fidelity graph neural networks for reducing training-data cost while improving mesh-based PDE surrogate accuracy on complex geometries.
Recommended citation: Taghizadeh, M., Nabian, M. A., & Alemazkoor, N. (2024). "Multifidelity Graph Neural Networks for Efficient and Accurate Mesh-based Partial Differential Equations Surrogate Modeling." Computer-Aided Civil and Infrastructure Engineering.
Published in Reliability Engineering & System Safety, 2023
A probabilistic transportation-resilience study integrating hazard, risk, network, and agent-based models for emergency medical response after earthquakes.
Recommended citation: Taghizadeh, M., Mahsuli, M., & Poorzahedy, H. (2023). "Probabilistic Framework for Evaluating the Seismic Resilience of Transportation Systems during Emergency Medical Response." Reliability Engineering & System Safety.
Published in International Journal for Uncertainty Quantification, 2023
A surrogate-modeling and uncertainty-quantification paper introducing a backward-greedy strategy for more accurate and efficient optimal experimental design.
Recommended citation: Taghizadeh, M., Xiu, D., & Alemazkoor, N. (2023). "Improving Accuracy and Computational Efficiency of Optimal Design of Experiment via Greedy Backward Approach." International Journal for Uncertainty Quantification.
Under Review
Published in Under review at Hydrology and Earth System Sciences, 2025
An under-review end-to-end GNN surrogate for rainfall-driven hydraulic prediction in urban stormwater systems.
Recommended citation: Zandsalimi, Z., Taghizadeh, M., Lynn, S. L., Goodall, J. L., Shafiee-Jood, M., & Alemazkoor, N. (under review). "End-to-End Graph Neural Networks for Real-Time Hydraulic Prediction in Stormwater Systems." Hydrology and Earth System Sciences.
Published in Under review at Engineering Applications of Artificial Intelligence, 2025
An under-review survey of graph neural network applications across transportation, power, water, and structural infrastructure systems.
Recommended citation: Anand, H., Khayambashi, K., Zandsalimi, Z., Taghizadeh, M., Hasnat, M. A., & Alemazkoor, N. (under review). "Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems." Engineering Applications of Artificial Intelligence.
