Bio
David Wagg is Professor of Nonlinear Dynamics at the University of Sheffield. His research interests are focused on the dynamics and control of engineering systems including the design, implementation and interoperation of digital twins.
Since November 2022, David has been part-seconded to the Alan Turing Institute in London where he is a Co-Director for the Turing Research and Innovation Cluster for Digital Twins.
In August 2023 David launched Digital Twinning NetworkPlus (DTNet+) an inclusive, diverse & multi-disciplinary UK-wide network with research interests that will transform the UK’s national capability in digital twins. The remit of DTNet+ spans across the entire domain of UKRI research and will explore the human, societal, legal, and ethical aspects of digital twins. The project is funded by EPSRC,UKRI until July 2028, please find more information at UKRI Digital Twinning NetworkPlus: DTNet+ (dtnetplus.ac.uk)
Prior to his appointment at the University of Sheffield, David was Professor in the Department of Mechanical Engineering at the University of Bristol. From 2004-2009 he was an EPSRC Advanced Research Fellow. He was awarded his PhD from University College London in 1998.
Research interests
The performance of engineering systems is governed by how well they behave in their operating environment. For a significant number of applications, including wind power, land transport, aerospace and large civil infrastructure, dynamic effects can dominate the operational regime.
In response to the urgent societal need to find technological solutions for global issues such as climate change, the performance envelope for engineering systems is being pushed to new levels. As a result understanding, & controlling dynamic behaviour is crucial for ensuring that we have safe, reliable and efficient engineering systems in the future.
David's current research activities include developing techniques for the design, implementation and interoperation of digital twins. Quantifying uncertainties within a dynamic digital twin context is a major topic of interest, that relates to the overall objective of validation of digital twin outputs. Ontological knowledge models (and neurosymbolic AI more widely) for interoperation of digital twins is also an active area of interest.