Project Level: Winter

Project Duration:

4 weeks – 20-36 hours per week. Applicant will be required on-site for the project.


“Science is a differential equation.” – Alan Turing

Differential equations offer a powerful modelling tool for understanding our world. They have diverse applications in domains including fluid flow, electromagnetism, epidemiology. However, many differential equations are difficult to solver, whether analytically or numerically. Machine learning has recently been applied to develop new solution methods. This project will explore some ideas in this direction.

Expected Outcomes:

  • Learn basics of machine learning.
  • Develop skills for implementing machine learning models for solving differential equations.
  • Develop skills in using existing tools
  • Develop skills in research design, implementation, experimentation, and communication.
  • A report documenting the work done and the findings.

Suitable for:

Essential: knowledge of differential equations.

Desirable: knowledge of machine learning and deep learning.

Further information:

Email Dr Nan Ye for any inquiries on the project.

Project members

Dr Nan Ye

Lecturer in Statistics&Data Science
School of Mathematics and Physics