Evangelos Papatheou

Lecturer

University of Exeter

United Kingdom

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Evangelos Papatheou is a Lecturer at the University of Exeter, UK. His research areas encompass advanced methodologies in data-driven analysis, including dynamic rock characterisation for drilling systems and the application of machine learning in medical diagnostics and engineering. Recent contributions to the field include studies on vibrational capsule technology for early cancer diagnosis and the optimisation of percussive drilling techniques.

Articles (14)

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2025

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2025

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2024

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2023

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2023

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2022

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2022

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2022

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2021

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2021

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2021

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2020

Collaborators (7)

RAJA SEKHAR BATTU

Delft University of Technology

NETHERLANDS

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Lawrence Bull

University of Glasgow

UNITED KINGDOM

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Yang Liu

Associate Professor in Dynamics and Control

University of Exeter

UNITED KINGDOM

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Julian Mauricio Londoño Monsalve

Lecturer in Mechanical Engineering

University of Exeter

UNITED KINGDOM

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Paul Gardner

University of Sheffield

UNITED KINGDOM

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Timothy Rogers

University of Sheffield

UNITED KINGDOM

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Elizabeth Cross

University of Sheffield

UNITED KINGDOM

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Evangelos Papatheou

Research Interests

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About

Articles (14)

A data-driven dynamic method of downhole rock characterisation for the vibro-impact drilling system

Combining transfer learning and numerical modelling to deal with the lack of training data in data-based SHM

Drilling performance analysis of a polycrystalline diamond compact bit via finite element and experimental investigations

Two-stage machine learning models for bowel lesions characterisation using self-propelled capsule dynamics

AI-Assisted Dynamic Tissue Evaluation for Early Bowel Cancer Diagnosis Using a Vibrational Capsule

Bayesian modelling of multivalued power curves from an operational wind farm

Machine learning-based rock characterisation models for rotary-percussive drilling

Feature-based intelligent models for optimisation of percussive drilling

Application and comparison of feature-based classification models for multistable impact motions of percussive drilling

LSTM-based approach for predicting periodic motions of an impacting system via transient dynamics

Foundations of population-based SHM, Part I: Homogeneous populations and forms

On the transfer of damage detectors between structures: An experimental case study

Real-time novelty detection of an industrial gas turbine using performance deviation model and extreme function theory

A Brief Introduction to Recent Developments in Population-Based Structural Health Monitoring

Collaborators (7)

RAJA SEKHAR BATTU

Lawrence Bull

Yang Liu

Julian Mauricio Londoño Monsalve

Paul Gardner

Timothy Rogers

Elizabeth Cross

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