Mr. Runze Yang
Mr. Runze Yang
I am a PhD candidate at Monash University with a background in computer science and expertise in machine learning. My research focuses on developing AI surrogate models to replace traditional DEM simulations, aiming to achieve faster computation while maintaining physical accuracy. I am particularly interested in physics-informed approaches that integrate deep learning methods, such as Transformers and graph-based models, with CFD-DEM to model complex fluid–particle systems.
Qualifications
- Master of Software Engineering, The University of Sydney, 2023
Expertise
- Machine Learning, Deep Learning, Computer Vision
Strong background in computer science with expertise in developing AI models, including neural networks, Transformers, and graph-based architectures. Experienced in applying machine learning techniques to complex datasets, model optimization, and large-scale distributed training, with a focus on building efficient and interpretable models.
Research Interests
Physics-informed AI surrogate modeling for multiphase flow and particle dynamics, focusing on integrating CFD-DEM simulations with deep learning approaches to enhance computational efficiency and maintain physical consistency.
Yang, R. (2024). Improving Lung X-ray Image Segmentation with U-Net and GSConv Module. Proceedings of the 6th International Conference on Computing and Data Science (CONF-CDS 2024). Published in Applied and Computational Engineering.