Navid Zobeiry, PhD

Navid Zobeiry

Assoicate Professor @ University of Washington

I am an associate professor in the Materials Science and Engineering department at the University of Washington. I am also an adjunct professor in the Aeronautics and Astronautics department. My research team merges materials science, data science, and advanced manufacturing, focusing on three main areas: 1) Smart testing methods combining physics-informed machine learning and traditional techniques; 2) Smart manufacturing using automation, sensing, and machine learning; 3) Smart engineering methods to optimize and accelerate aerospace design, certification, and qualification through machine learning, automated testing, and multi-fidelity simulations. Our research is anchored in a novel machine learning framework that blends probabilistic and deterministic approaches, integrating engineering data and physical laws. This approach has led to several patented AI innovations.

research activities

Online Courses ↑

This 9-lecture series introduces the principles of Physics-Informed Machine Learning (PIML) and its application to solving complex multi-physics problems in science and engineering.

This 8-lecture series on composite structural engineering is designed for training the next generation of aerospace engineers, focusing on the certification of composite civil aircraft structures per FAA curriculum standards.

Recent Awards ↑

Kendall Johnson and Rowan Preedy received the Technical Paper Award at SAMPE 2026.

Max Fu and Kendall Johnson received the Best Paper Award from the American Society for Composites.

Navid Zobeiry recives the certificate for Boeing Distinguished Researcher & Scholar Seminar Series (B-DRASS).

Caleb Schoenholz takes SAMPE URS third Best Paper Award.

Selected Publications ↑

Fu, H., Eskandariyun, A., Portales Picazo, P., Johnson, K. A., Morton, A., Wynn, M., & Zobeiry, N. (2026). Artificial intelligence and machine learning in composite materials: A comprehensive literature review and bibliometric analysis. Composites Part A: Applied Science and Manufacturing, 210, 110092.

Schoenholz, C., Cheng, R., Fu, H., & Zobeiry, N. (2026). Uncertainty-aware accelerated characterization of process-dependent composite properties using probabilistic machine learning. Composites Part A: Applied Science and Manufacturing, 210, 110039.

Hassani, F., Fu, H., Zobeiry, N., & Malek, S. (2026). Semi-auxetic cellular laminate: A novel lightweight stiff material. Composite Structures, 386, 120287.

Zobeiry, N. (2026). Differentiable multiphysics co-optimization via implicit neural representations: A transient hamburger-cooking benchmark. arXiv:2605.01040.

Fu, H., & Zobeiry, N. (2026). Data-driven machine learning meta-analysis of process–property relationships in polymer additive manufacturing: A case study on FFF-printed PEEK. Journal of Manufacturing Processes, 163, 100–113.

Zappino, E., Santori, M., Zobeiry, N., & Petrolo, M. (2026). Temperature and degree of cure 3D distributions in fiber-reinforced and sandwich composites using 1D finite elements. Mechanics of Materials, 217, 105651.

Morton, A., & Zobeiry, N. (2026). Machine learning–statistical inference of prepreg conditioning history from surface morphology. Composites Science and Technology, 275, 111494.

Portales P., Gray A., Zobeiry N. (2025). Efficient characterization and optimization of pyrolysis in carbon-carbon composites through machine learning, Composites Part A, 190: 108664.

Fontes A., Zobeiry N., Shadmehri F. (2025). Theory-guided machine learning for thermal modeling of in-situ automated fiber placement of thermoplastic composites, Composites Science and Technology, 260:110987.

Schoenholz, C., Zappino, E., Petrolo, M., & Zobeiry, N. (2024). Efficient analysis of composites manufacturing using multi-fidelity simulation and probabilistic machine learning. Composites Part B: Engineering, 280, 111499.

Wynn, M., Oster, L., Chase, G., Salviato, M., & Zobeiry, N. (2024). Assessment of the effect of processing parameters on peel failure of laser-assisted automated fiber placed thermoplastic composites. Manufacturing Letters, 40, 93-96.

Zappino, E., Masia, R., Zobeiry, N., Petrolo, M., & Carrera, E. (2024). Development of mitigation strategies for process-induced deformations through finite elements. Mechanics of Advanced Materials and Structures, 1-13.

Schoenholz, C., & Zobeiry, N. (2024). Investigating the Impacts of Processing Variability on Tool-part Interaction for Interply-toughened Aerospace Composites using a Novel Shear Technique. Composites Part A: Applied Science and Manufacturing, 178, 107973.

Schoenholz, C., & Zobeiry, N. (2024). An Accelerated Process Optimization Method to Minimize Deformations in Composites Using Theory-guided Probabilistic Machine Learning. Composites Part A: Applied Science and Manufacturing, 176, 107842.

Wynn, M., & Zobeiry, N. (2022). Investigating the Effect of Temperature History on Crystal Morphology of Thermoplastic Composites Using In Situ Polarized Light Microscopy and Probabilistic Machine Learning. Polymers, 15(1), 18.

Lee, A., Wynn, M., Quigley, L., Salviato, M., & Zobeiry, N. (2022). Effect of temperature history during additive manufacturing on crystalline morphology of PEEK. Advances in Industrial and Manufacturing Engineering, 4, 100085.

Humfeld, K. D., Gu, D., Butler, G. A., Nelson, K., & Zobeiry, N. (2021). A machine learning framework for real-time inverse modeling and multi-objective process optimization of composites for active manufacturing control. Composites Part B: Engineering, 223, 109150.

Zobeiry, N., & Humfeld, K. D. (2021). A physics-informed machine learning approach for solving heat transfer equation in advanced manufacturing and engineering applications. Engineering Applications of Artificial Intelligence, 101, 104232.