Mr. Runze Yang
Mr. Runze Yang
I am a PhD candidate at Monash University with a background in computer science and specialized in machine learning. My research focuses on developing AI surrogate models in DEM simulations, aiming to achieve faster computation while maintaining physical accuracy. I am particularly interested in physics-informed approaches that integrate deep learning methods with DEM to model complex particle systems.
Qualifications
- Master of Software Engineering, The University of Sydney, 2023
Expertise
- Machine Learning, Deep Learning
Strong background in computer science with expertise in developing AI models. Experienced in applying machine learning techniques to complex datasets, model architecture, 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.