Scholar
Jiaxiang Yi
Google Scholar ID: LM6O83QAAAAJ
PhD Candidate at Faculty of Mechanical Engineering
Citations & Impact
Citations
467
H-index
10
i10-index
10
Publications
20
Co-authors
6
list available
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Research Experience
- Current research focuses on developing uncertainty quantification and multi-fidelity modeling methods, applied to problems in data-driven mechanics, particularly in constitutive modeling and the design and analysis of recycled polymers.
Education
- PhD candidate at the Department of Mechanical Engineering, Delft University of Technology (TU Delft); Bachelor's and Master's degrees from Huazhong University of Science and Technology (HUST), where his research during the Master's program centered on developing active-learning surrogate models for reliability analysis and Bayesian optimization for engineering design.
Background
- Research interests include developing uncertainty quantification and multi-fidelity modeling methods for machine learning models, with a focus on data-driven mechanics, particularly in constitutive modeling and the design and analysis of recycled polymers.
Miscellany
- Long-term goal is to develop trustworthy and interpretable machine learning models featuring principled uncertainty quantification, aiming to address real-world problems in engineering and science, including but not limited to the autonomous and reliable design of complex materials ranging from recycled polymers to high-performance composites.
