Machine learning for physical systems

Research Lab

Developing machine learning approaches for complex physical systems, from structural dynamics and health monitoring to scientific computing.

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  • Structural dynamics
  • Scientific machine learning
  • Structural health monitoring
  • Fluid–structure interaction
Surrogate Modelling for Structural Dynamics
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Surrogate Modelling for Structural Dynamics

A public overview of surrogate modelling for transient structural response under extreme fluid-driven loading.

PhD research — public overview · Current

Surrogate ModellingStructural DynamicsFSIScientific ML
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Completed Academic Work01
Deep Learning Approach to Impact Detection in Sensorised Panels
Case StudyCompleted

Deep Learning Approach to Impact Detection in Sensorised Panels

A fully data-driven machine-learning framework for impact localisation and classification on a cylindrical composite hydrogen tank, using passive sensing and physics-guided feature engineering.

Structural Health MonitoringImpact DetectionXGBoostComposite Structures
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