Research Student
Academic Division: Mechanics, Materials and Design
Research group: Applied Mechanics
Email: mjb314@cam.ac.uk
Research interests
Vibration, dynamics, nonlinearity, control, acoustics, physics-informed machine learning.
Research projects
PhD: ‘Hybrid machine learning and physics-based modelling for active noise control in automotive vehicles'
Supervised by Dr Tore Butlin and Dr Ole Nielsen, funded by Bose.
I incorporate underlying physics into machine learning (ML) architectures to improve performance in active noise control (ANC), which requires models to be both accurate and fast. This combines expertise in physical domains (nonlinear dynamics, vibration, acoustics) with control theory and data-driven ML methods to solve complex problems e.g. real-time prediction of sound through a vehicle. These tailored physics-ML architectures aim to show a benefit over purely physics or black box data-driven methods.
Teaching activity
- Part I Supervisions:
- IA Mechanics & Mechanical Vibrations (Magdalene)
- IB Mechanics (Clare)
- Part IIA Supervisions (Department of Engineering):
- 3C5 Dynamics
- 3C6 Vibration
- 3F1 Signals & Systems, 3F2 Systems & Control (occasional cover)
- SC2 Bicycle Design Projects
- Undergraduate Admissions (Magdalene College)
Biography
I completed my MEng (with Distinction) in Mechanical and Information Engineering at Magdalene College, Cambridge, where I remain as a PhD Student and Senior Scholar.
Department role and responsibilities
- Organiser of the Dynamics and Vibration Group Tea Time Talks (2025-26)