Sitemap
This page lists the main published pages and collection items on the site. An XML sitemap is also available for search engines and other crawlers.
Pages
portfolio
Multi-fidelity GNNs for Flood Hazard Mapping
A coarse-to-fine graph learning framework for high-resolution flood hazard mapping on unstructured meshes.
Geometry-Informed Neural Operators for Flood Forecasting
A transferable flood forecasting framework that combines geometry-informed neural operators with domain adaptation for rapid prediction on new river segments.
HydroGraphNet and NVIDIA PhysicsNeMo
A physics-informed and interpretable flood forecasting model that improves accuracy, mass conservation, and operational reliability on unstructured meshes.
Multi-fidelity GNN Surrogates for Probabilistic Power Flow
A graph-based surrogate modeling approach for repeated probabilistic power flow analysis under demand and renewable uncertainty.
Connected-Vehicle Data for Proactive Safety Analytics
A multimodal safety analytics pipeline built on more than 15.6 billion connected-vehicle events to support infrastructure and enforcement decisions.
Graph-based Diffusion for Constrained Optimization
Ongoing postdoctoral work on diffusion models, differentiable optimization, and feasibility-aware decision-making for resilient power and infrastructure systems.
Transportation Resilience for Emergency Medical Response
A probabilistic framework for evaluating how transportation disruption affects emergency medical response and social loss after earthquakes.
Transportation Risk and Recovery for Community Resilience
A probabilistic recovery framework for quantifying transportation-system risk, loss, and resilience at the community scale.
Optimal Design of Experiments for Surrogate Modeling
A backward-greedy sampling strategy for improving the efficiency and accuracy of polynomial-chaos surrogate construction.
End-to-End GNNs for Stormwater Systems
A spatiotemporal graph neural network surrogate for rainfall-driven hydraulic prediction in urban stormwater networks.
Stormwater Digital Twins and Constrained Real-Time Control
Ongoing postdoctoral research on uncertainty-aware flood-state reconstruction and projected diffusion policies for stormwater real-time control.
Flood Sensing, Bayesian Inference, and Sensor Design
Ongoing postdoctoral research on physics-consistent flood-state inference and value-of-information sensor network design.
Neural-Operator Domain Decomposition for Flood Forecasting
Ongoing work on local-to-global neural-operator surrogates for fast shallow-water flood forecasting and uncertainty quantification.
publications
Improving Accuracy and Computational Efficiency of Optimal Design of Experiment via Greedy Backward Approach
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.
Probabilistic Framework for Evaluating the Seismic Resilience of Transportation Systems during Emergency Medical Response
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.
Multifidelity Graph Neural Networks for Efficient and Accurate Mesh-based PDE Surrogate Modeling
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.
Multi-fidelity Graph Neural Networks for Efficient Power Flow Analysis under High-Dimensional Demand and Renewable Generation Uncertainty
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.
Multi-fidelity Physics-informed Generative Adversarial Network for Solving Partial Differential Equations
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.
Interpretable Physics-Informed Graph Neural Networks for Flood Forecasting
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.
Applications of Graph Neural Networks in Civil Infrastructures: A Review on Transportation, Power, Water, and Structural Systems
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.
Multi-Fidelity Graph Neural Networks for Efficient and Accurate Flood Hazard Mapping
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.
End-to-End Graph Neural Networks for Real-Time Hydraulic Prediction in Stormwater Systems
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.
FloodForecaster: A Domain-Adaptive Geometry-Informed Neural Operator Framework for Rapid Flood Forecasting
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.
Probabilistic Modeling of the Risk and Recovery of the Transportation Systems for Community Resilience Analysis
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.