Catalogue of Possibilities / Supervisors

Dr Khiem Nguyen

Study and design smart and functional materials

Lecturer in Multiscale Materials

James Watt School of Engineering

Research interests:

Data-driven computing for solid mechanics, Physics-informed neural networks for computational mechanics, Multi-scale materials, Metamaterials, Two-scale topology optimization

Research fields:

Physics-informed neural networks, Finite element analysis, Numerical methods, Metamaterials

Mission Priority Areas

Why do you want to join the DiveIn community?

I have a background in Mathematics and Computational Mathematics. I have also been teaching Introduction to Machine Learning and Advanced Machine Learning at the James Watt School of Engineering since 2023. I believe that my computational background and experience can contribute to interdisciplinary research that involves simulations and deep learning. Equally important, I believe that the selected PhD students from the Diveln CDT are bright and motivated students and that I can expand my knowledge from them and other colleagues. By joining Diveln, I can be exposed to other like-minded people who may find my research expertise useful for their research problems.

Personal profile:

Since I have a background in Mathematics and Computational Mathematics, I am interested in various research areas that involve developing mathematical models and theories for the simulation and prediction of physical models. I am an ERC and currently supervising one PhD student. Our research focuses on designing new functional cellular materials for various physical applications. We would like to connect with other researchers who need to use various forms of deep neural networks or machine learning techniques in general.

I am interested in solid mechanics and computational techniques for multi-physics coupling materials such as piezoelectric materials, electromagneto-mechanically coupled materials. I am also interested in dispersive nonlinear waves and nonlinear dynamics from a mathematical point of view. Indeed, I have contributed simulations and predictions for some experimental works. I am particularly interested in developing mathematical models so that it can predict and explain the results for experiments.

These days, I am interested in applying convolution neural networks and recurrent neural networks to robotics. I can supervise projects that involve heavy simulations and mathematical formulation.

I see myself in the “postdoc mode” because I have always written my own code in my research and contributed codebase to all of the publications I authored and coauthored. This has been said by several PhD students who worked with me. In fact, currently my PhD student and I are writing code together and publishing them on github.

I must admit that I don’t have much track record/experience in EDI. However, I have published works with different international researchers at different institutions. I enjoy the international collaboration atmosphere.

Due to my background in Mathematics, I have worked in some different fields, including applied mathematics, computational mechanics, and nonlinear dispersive waves. I believe being able to tap into different research areas and make contributions is my strength. My publications can be found on Google Scholar and ResearchGate

https://www.gla.ac.uk/schools/engineering/staff/khiemnguyen/

https://scholar.google.com/citations?user=A4cM72cAAAAJ&hl=en

https://github.com/khiemnguyen0407