Hadi Meidani

For More Information

• Computational Intelligence for Engineering Lab (CIEL)

Education

• Ph.D., Civil Engineering, University of Southern California, 2012
• M.S., Electrical Engineering, University of Southern California, 2012
• M.S., Civil Engineering, University of Southern California, 2011
• M.S., Structural Engineering, Sharif University of Technology, Iran, 2005
• B.S., Civil Engineering, K.N.Toosi University of Technology, Iran, 2002

Biography

Hadi Meidani is an Associate Professor in the Department of Civil and Environmental Engineering at the University of Illinois at Urbana-Champaign (UIUC). His research focuses on physics-informed AI, and digital twins for engineering design. He earned his Ph.D. in Civil Engineering, his M.S. in Electrical Engineering, and his M.S. in Structural Engineering from the University of Southern California (USC). Prior to joining UIUC, he was a postdoctoral scholar in the Department of Aerospace and Mechanical Engineering at USC and in the Scientific Computing and Imaging Institute at the University of Utah. Dr. Meidani is the Chair of the Machine Learning Committee of the ASCE Engineering Mechanics Institute. He is the recipient of an NSF CAREER Award on fast computational models for infrastructure networks. His team have won awards from data competitions on railroad engineering and his research has been sponsored by federal agencies such as National Science Foundation (NSF), DOE, and DOT.

Academic Positions

• Postdoctoral Research Associate, Scientific Computing and Imaging Institute, University of Utah, 2013-2014
• Postdoctoral Research Associate, Department of Aerospace and Mechanical Engineering, University of Southern California, 2012-2013

Research Statement

My research aims to transform how engineering systems are modeled, designed, and operated by advancing a new paradigm of AI-driven scientific computing. Through support from agencies such as NSF, DOE, and industry partners, my group has advanced physics-informed machine learning, neural operators, and graph-based AI models to dramatically accelerate traditional simulation and enable fast, scalable simulation and design. Through a cohesive body of publications and closely integrated student research, we are building generalizable AI/ML models that learn physical laws, adapt across systems, and enable digital twins at scale and apply these methods to problems in transportation systems, structural mechanics, and emerging areas such as biomedical systems.

Research Interests

• Resilient infrastructure systems
• Design optimization
• Surrogate-based modeling and design
• Physics-informed machine learning
• Digital twins for infrastructure systems
• AI surrogates for engineering design

Selected Articles in Journals

• Nabian, M. A., and H. Meidani (2019). "A Deep Learning Solution Approach for High-Dimensional Random Differential Equations". Probabilistic Engineering Mechanics, 57, 14-25.
• N. Alemazkoor and H. Meidani (2018). "A Near-Optimal Sampling Strategy for Sparse Recovery of Polynomial Chaos Expansions". Journal of Computational Physics 371, 137-151.
• N. Alemazkoor and H. Meidani (2018). "A Preconditioning Approach for Improved Estimation of Sparse Polynomial Chaos Expansions". Computer Methods in Applied Mechanics and Engineering, 342, 474-489.
• M. Nabian, H. Meidani (2018). "Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks", Computer-Aided Civil and Infrastructure Engineering, 33 (6), 443-458.
• N. Alemazkoor and H. Meidani (2017). "Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions", Computer Methods in Applied Mechanics and Engineering, 318, pp. 937-956.
• H. Meidani, J.B. Hooper, R.M. Kirby and D. Bedrov (2017). "Calibration and Ranking of Coarse-grained Water Models using the Bayesian Formalism", International Journal for Uncertainty Quantification, 7(2): 99-115.
• V. Keshavarzzadeh, H. Meidani, D. Tortorelli (2016). "Gradient Based Design Optimization under Uncertainty via Stochastic Expansion Methods", Computer Methods in Applied Mechanics and Engineering, 306: 47–76.
• H. Meidani and R. Ghanem (2015). "Random Markov Decision Processes for Sustainable Infrastructure Systems", Structure and Infrastructure Engineering, Maintenance, Management, Life-Cycle Design and Performance 11 5: 655-667.
• H. Meidani and R. Ghanem (2014). "Multiscale Markov Models with Random Transitions for Energy Demand Management", Energy and Buildings. 61. pp 267274.
• H. Meidani and R. Ghanem (2014). "Spectral Power Iterations for the Random Eigenvalue Problem", AIAA Journal, Vol. 52, No. 5 (2014), pp 912-925.
• H. Meidani and R. Ghanem (2012). "Uncertainty Quantification for Markov Chain Models", Chaos: An Interdisciplinary Journal of Nonlinear Science. 22(4).
• M.I. Todorovska, H. Meidani, M.D. Trifunac (2009). "Wavelet Approximation of Earthquake Strong Ground Motion - Goodness of Fit for a Database in Terms of Predicting Nonlinear Structural Response". Soil Dynamics and Earthquake Engineering, 29(4), pp 742-751.

Articles in Conference Proceedings

• H. Meidani and R. Ghanem (2013). "Uncertainty Quantification of Diffusion Maps" 11th International Conference on Structural Safety & Reliability (ICOSSAR 2013), New York, NY.
• H. Meidani and R. Ghanem (2012). "Robust Decision Making for Markov Chains in Random Environment". Proceedings of the 2012 Joint Conference of the Engineering Mechanics Institute and 11th ASCE Joint Specialty Conference on Probabilistic Mechanics and Structural Reliability (EMI/PMC 2012), Notre Dame, IN.
• H. Meidani and R. Ghanem (2012). "A Stochastic Modal Decomposition Framework for the Analysis of Structural Dynamics under Uncertainties". Proceedings of the AIAA 53rd Structures, Structural Dynamics, and Materials Conference, Honolulu, HI.