Project Level: Summer

Project Duration: 6 weeks

Hours of Engagement: 20-36 hours

Project Description:

“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. Neural networks have recently been shown to be promising efficient approximate solutions. This project will explore some ideas in this direction.

Expected Outcomes:

• Develop skills for implementing neural network 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 and neural networks

Desirable: knowledge of numerical methods for solving differential equations

Contact for further information:

Dr Nan Ye: 

Project members

Dr Nan Ye

Lecturer in Statistics&Data Science
School of Mathematics and Physics