Staff profile

Mao Ouyang is a postdoctoral research associate in the Department of Engineering at Durham University, specialising in experimental and computational geomechanics, geoscience, earth observations, atmospheric science, natural hazards and data assimilation. He completed his PhD on quantifying internal erosion using a newly developed plane strain erosion apparatus. Before joining Durham University, he gained extensive research experience in geoscience and natural hazards at the University of Tokyo and in numerical weather prediction at Chiba University.

Mao currently contributes to the EPSRC-funded project (EP/X024849/1) on braced excavations. He developed an in-house Julia finite element code for high-fidelity simulations of braced excavations and subsequently employed cutting-edge AI techniques to develop reduced order models for real-time prediction. His expertise in high-performance computing has enabled him to secure computational resources through the UKRI HPC Call 2025 (Application No. APP95205) and the UKRI AI Research Resource (AIRR) in 2026 as Project Lead. He is also actively involved in education and student development, supporting N8 CIR-funded summer internship, dissertation projects, and PhD student supervision.

Conference Paper

• Reduced Order Models for geotechnical predictions
  
  Ouyang, M., Petalas, A., Coombs, W., Augarde, C., Alagha, A., Knappett, J., & Brown, M. (2026). Reduced Order Models for geotechnical predictions. In Proceedings of the 21st International Conference on Soil Mechanics and Geotechnical Engineering. https://doi.org/10.53243/ICSMGE2026-596
• Data-driven reduced order models for multi-dimensional data
  
  Ouyang, M., Augarde, C., Coombs, W., Petalas, A., Knappett, J., Brown, M., & Alagha, A. (2026, April 9 – 2026, April 10). Data-driven reduced order models for multi-dimensional data [Conference paper]. Presented at UK Association for Computational Mechanics Conference (UKACM2026), Liverpool, UK.
• Developing Efficient Data-driven Predictive Tools for Geotechnics. Presented as the 18th International Conference on Computational Plasticity
  
  Ouyang, M., Petalas, A., Coombs, W., & Augarde, C. (2025, September 2). Developing Efficient Data-driven Predictive Tools for Geotechnics. Presented as the 18th International Conference on Computational Plasticity [Conference abstract]. Presented at 18th International Conference on Computational Plasticity (COMPLAS2025), Barcelona, Spain.

Journal Article

• A reduced order model framework suitable for geotechnical problems
  
  Ouyang, M., Augarde, C., Coombs, W., Petalas, A., Knappett, J., Brown, M., & Alagha, A. (2026). A reduced order model framework suitable for geotechnical problems. Acta Geotechnica. Advance online publication. https://doi.org/10.1007/s11440-026-03221-0
• Finite Element Bayesian Inference Using Dual Ensemble Kalman Filters for Uncertainty Reduction in Geotechnical Models
  
  Augarde, C., Ouyang, M., Coombs, W., Petalas, A., & Alagha, A. (2026). Finite Element Bayesian Inference Using Dual Ensemble Kalman Filters for Uncertainty Reduction in Geotechnical Models. Geotechnical and Geological Engineering, 44, Article 332. https://doi.org/10.1007/s10706-026-03831-1

Presentation

• Improved Inference of Soil Behaviour and Model Parameters In Geotechnics Using Bayesian Methods
  
  Ouyang, M., Augarde, C., Coombs, W., Petalas, A., Knappett, J., Brown, M. J., & Alagha, A. (2025, April 23 – 2025, April 25). Improved Inference of Soil Behaviour and Model Parameters In Geotechnics Using Bayesian Methods. Presented at UK Association for Computational Mechanics Conference (UKACM2025), London, UK.