Keynote Speakers
Professor Lee Margetts
UKAEA Chair of Digital Engineering for Fusion Energy, University of Manchester
Director of Fusion Engineering Centre for Doctoral Training
Professor Lee Margetts is UKAEA Chair of Digital Engineering for Fusion Energy at The University of Manchester and Director of the Fusion Engineering Centre for Doctoral Training, helping to develop the next generation of researchers and engineering leaders for the fusion sector.
His work brings together expertise in imaging, simulation, high-performance computing, artificial intelligence and digital twins to transform how complex engineering systems are designed, operated and maintained.
Over the past two decades, the research groups and collaborations he has been part of have helped pioneer approaches that bridge the physical and digital worlds, from X-ray tomography and laser-based imaging to image-derived simulation and intelligent digital engineering. This work has been driven by a fundamental challenge: how can observations of the real world be transformed into predictive models that support better engineering decisions?
Today, he works with researchers, industry partners, national laboratories and government organisations to develop the digital technologies needed to accelerate fusion energy, including digital twins, advanced simulation capabilities and AI-enabled engineering systems. These collaborations are helping to create the engineering intelligence required for some of the most ambitious technological challenges of the twenty-first century.
Professor Margetts is passionate about the role that imaging, computation and AI can play in creating a new generation of engineering systems that can observe, understand and anticipate. His vision is that the technologies being developed for fusion today will not only help deliver clean, abundant energy, but will also redefine how humanity designs, operates and interacts with complex engineered systems in the decades ahead.
Abstract: From X-Rays to Digital Twins: Giving Fusion Power Plants a Nervous System
For centuries, engineers have built machines that are powerful, precise and transformative. Yet even the most advanced machines remain fundamentally passive. They do not truly understand their condition, anticipate their future, or learn from their experience. The next revolution in engineering is changing that.
Advances in imaging, sensing, simulation, high-performance computing and artificial intelligence are converging to create something entirely new: digital systems that can continuously observe, understand and predict the behaviour of complex physical assets. Technologies that once revealed hidden structures through X-rays and laser scanning are now part of a broader vision in which data is transformed into insight, and insight into action.
Fusion energy presents one of the greatest opportunities to realise this vision. Future fusion power plants will be among the most complex machines humanity has ever built. Their success will depend not only on breakthroughs in physics and materials science, but also on the creation of a digital nervous system: a living digital counterpart that continuously senses, learns and anticipates. The future of fusion is not simply about building a star on Earth. It is about giving that star the ability to understand itself.
Fusion is more than an energy challenge. It is a proving ground for a new generation of intelligent engineering systems capable of accelerating discovery, reducing uncertainty and transforming how complex technologies are designed, operated and maintained. If the twentieth century was defined by machines that amplified human strength, the twenty-first may be defined by machines that amplify human understanding.
This talk explores how the convergence of imaging, computation, digital twins and AI is reshaping engineering, and why fusion energy provides a unique opportunity to lead that transformation. The challenge now is not merely to build better reactors, but to create the digital intelligence that will enable them to evolve, adapt and improve. In doing so, we have the opportunity not only to accelerate the path to clean, abundant energy, but also to redefine the relationship between humans, machines and the engineered world.
Dr Stéphane Roux
Director of Research, CNRS
Stéphane Roux graduated from the Ecole Polytechnique in 1983 and the Ecole Nationale des Ponts et Chaussées (ENPC) in 1985. He received his Ph.D. in mechanical engineering from the ENPC in 1990. As a CNRS Research Professor, he served successively at the Ecole Supérieure de Physique et Chimie Industrielles de la Ville de Paris (ESPCI), at the joint CNRS/Saint-Gobain Research Laboratory, and currently, he is at the Laboratory of Mechanics Paris-Saclay at the Ecole Normale Supérieure de Paris-Saclay. His research activity is devoted to data processing and image-based measurements for experimental mechanics. This includes digital image correlation, stereo-correlation (for surface reconstructions in 3D), and also digital volume correlation for tomography. He holds 17 patents and is the author of more than 420 publications, (H=86, Google Scholar). He received the Silver Medal from the CNRS in 2006, and the Jaffé prize (French Academy of Sciences) in 2019.
Abstract: Yarn-path extraction in large 3D textiles based on periodicity and volume registration
Segmenting yarn paths in 3D woven reinforcements from low-resolution tomographic images is notoriously difficult and time-consuming, and is still performed manually today. The chosen example is the segmentation of warp and weft yarns at the root of a Leap fan blade, with a voxel size of 140 µm, and the image extending over the entire part.
The initial observation is that, in some regions, the weaving topology is periodic. From an autocorrelation analysis, a unit periodic cell is easily identified, and, by exploiting its periodicity, warp and weft yarns are segmented within the cell. This task is accomplished using local matching with a template of the yarn cross-section, exploiting the continuity of each yarn path and the consistency of the warp and weft paths in 3D space, with no overlap. The unit cell is tiled along the three space directions to create a reference volume of arbitrary size. Global Digital Volume Correlation registers the actual image with the periodic reference, accounting for large-scale distortions to match the two 3D images. The global approach is based on a fine-mesh discretization of the displacement field and mechanical regularization. These two features help avoiding local-minima trapping, as can be feared in an almost periodic medium. The mapping can now be used to transfer the unit-cell segmentation to the entire volume, thereby providing a sound labeling of each individual yarn.
Keynote Speakers
Keynote Speakers
Professor Stepan V. Lomov, KU Leuven, Belgium
XCT imaging and image quantification of fibrous materials
Stepan V. Lomov (1955) graduated from School N30 in Leningrad (1972), Phys.-Mech of Leningrad Polytechnic Institute (1978). PhD on terminal ballistics (1985), Dr Hab. on textile materials science (1995), St. Petersburg State University of Technology and Design. Since 1999 works in KU Leuven, Belgium, Department of Materials Engineering, coordinator of the Composite Materials Group in 2013 – 2020, Toray Professor in 2015 – 2020. Professor Emeritus since 2020. The talk is mainly based on the research conducted in the Department of Materials Engineering, KU Leuven, in collaboration with other groups worldwide.
Professor Luisa Silva, Centrale Nantes, France
Luisa Silva’s research focus is on advanced numerical techniques used in High Performance Computing, including stable/stabilised immersed finite element methods, interface capturing through modified level-set or phase-field methods, anisotropic mesh adaptation, and developments in a massively parallel context.
Her expertise fields and software application developments are as follows:
– Finite elements and Eulerian approaches to solve thermomechanical multiphase problems.
– Parallel multiphase computational fluid dynamics of highly viscous to inviscid fluid flows and their phase changes.
– Direct 3D image-based numerical simulations (X-Ray tomography, point clouds, etc.) in several application fields.
– Development of software applications for urban environments, material forming processes and material structure development simulations.
Training Course Facilitators
Professor David Rousseau, Université d’Angers, France
A practical introduction to Deep learning applied to image processing
David Rousseau (Member, IEEE) was born in France, in 1973. He received the M.S. degree in physics and signal processing from the Institut de Recherche Coordination Acoustique et Musique, Paris, France, in 1996, and the Ph.D. degree in signal and image processing from the Université d’Angers, Angers, France, in 2004. From 2010 to 2017, he was a Full Professor of image processing applied to bioimaging with CREATIS, Université Lyon 1, Lyon, France. Since 2018, he has been heading the Bioimaging Research Group (ImHorPhen), Université d’Angers. His research interests include information sciences, machine learning-based computer vision, and their applications to life sciences.
Dr Félix Mercier, Université d’Angers, France
A practical introduction to Deep learning applied to image processing
Dr Chris Richardson, University of Cambridge, UK
FEniCS: a popular Open Source software package for Finite Element Analysis
Chris has been collaborating on research into modelling of continuum problems using the Finite Element Method (FEM) and development of the popular FEniCS software platform. Recent work is in partnership with other institutions through the UKRI ExCALIBUR Programme, and the Coupling, Synthesis and Performance Project and High Priority Use Case SysGenX. He is also working on benchmarking for High Performance Computing, GPU programming, and working with Rolls-Royce future systems team for engine design.
Additional Speakers
- Jérôme Adrien, CNRS, INSA Lyon, Université Claude Bernard Lyon 1
- Miraslau Barabash, University College London
- Michele Darrow, Rosalind Franklin Institute
- Simon Daubner, Imperial College London
- Lloyd Fletcher, UK Atomic Energy Authority
- Grammatiki Lioliou, University College London
- Nina Lassalle-Astis, Cetim
- Andrea Mazzolani, University College London
- Vincent Maes, University Of Bristol
- Hannah Robarts, UKRI-STFC
- Renee Taylor, University of Sheffield
- Léonard Turpin, Université De Bordeaux
- Franck Vidal, UKRI-STFC / Bangor University
- Nicola Wadeson, The University of Manchester
Poster Presenters
- Jean-Claude Bikaku, University of Leeds
- Ethan Edmunds, The University Of Sheffield
- Harriet Jones, UKRI-STFC
- Lizzie Mushangwe, University of Oxford
- Zeyan Wang, University College London
Keynote Speakers
Dr Samuel J Cooper, Dyson School of Design Engineering, Imperial College London
Machine learning for the characterisation and design of battery electrodes
Dr Sam Cooper is an Associate Professor in energy science and materials design in the Dyson School of Design Engineering at Imperial College London. His PhD was on the characterisation and optimisation of battery and fuel cell electrodes through 3D imaging, simulation and machine learning. Sam is the leader of the TLDR (Tools for Learning, Design and Research) group who have a particular interest in the application of generative adversarial networks to design tasks.
Professor Christian Gasser, KTH Royal Institute of Technology, Stockholm
Integrating 2D/3D images with numerical simulations for mechanical deformation analysis
Christian Gasser is Professor of Biomechanics at KTH Royal Institute of Technology, Stockholm. He holds a Master of Mechanical Engineering (1997) and a PhD in Civil Engineering (2001), both from Graz University of Technology, Austria. The development and application of advanced numerical techniques to solve realistic (bio)engineering and clinical problems, is Gasser’s main research objective. Constitutive models for anisotropic finite strain materials have been implemented in all major Finite Element simulation packages, such as ANSYS, ABAQUS, COMSOL, etc, and Gasser’s translational biomechanics research led to A4clinicsRE, commercial biomechanical-based simulation software for clinical decision making. In 2022 he has been listed as KTH’s most influential researcher in Biomedical Engineering, and his work led so far to more than 16k Google Scholar citations. He received a Humboldt Research Award from Germany, and Gasser is designated 2024 Odqvist lecturer, a distinction awarded by the Swedish national mechanics committee. He is Associate Editor of Int. J. for Num. Meth. in Biomed. Engrg, in the editorial board of Mechanics of Soft Materials and a EMMCC member, principal founder of ARTEC Diagnosis AB and VASCOPS GmbH, and serves a legal expert for skiing accident reconstruction at Oberlandesgericht, Graz, Austria.
Special Invited Speaker
Dr Tim J. Barden, Rolls-Royce plc
Sensors and sensing – Getting to the start line for imaged-based simulation
Tim Barden moved to Rolls-Royce having carried out a post-doctorial position at Bath University researching thermal methods for non-destructive evaluation. His role at Rolls-Royce primarily involves the development and introduction of new NDE technologies and is the current Industrial Chair of the Research Centre in Non-Destructive Evaluation.
Image based simulation has potential benefits to many industries. However, as with all simulation, understanding the inputs is essential and for image based simulation they are varied and each application will have its own limitations. Non-destructive evaluation (NDE) utilises a wide scope of technologies, any physical phenomena that gives information about material integrity could be used as a NDE technique. The same can be said for obtaining data for image based simulation. Modalities include the electromagnetic spectrum, such as optical and x-rays, as well as vibrational waves such as ultrasonics. The variation is further increased by the numerous techniques to stimulate a test object, monitor the response and interpret the output.
The presentation will concentrate on the more common NDE techniques used for obtaining image data including radiography, x-ray computed tomography, ultrasonic and visual. Additionally, general topics will be considered including understanding the quality of the data and reducing artefacts.
Additional Speakers
- Ander Biguri, University Of Cambridge
- Christian Breite, KU Leuven
- Alex Cornell-Thorne, Swansea University
- Koussay Daadouch, Ruhr University Bochum
- Dongze He, The University of Manchester
- Umeir Khan, University Of Bristol
- Rhydian Lewis, Swansea University
- Grammatiki Lioliou, University College London
- Harry Lipscomb & Marti Puig, University of Manchester
- Iwan Mitchell, Bangor University
- Subrata Mondal, University of Bath
- Tessa Nogatz, RPTU Kaiserslautern-Landau
- Fatima Zahra Oujebbour, CNESTEN
- Chris Packer, The University of Edinburgh
- Sylwin Pawlowski, University of Lisbon
- Connie Qian, The University of Warwick
- Dirk Schut, Centrum Wiskunde & Informatica (CWI)
- Elena Syerko, Nantes Université, École Centrale de Nantes
- Benjamin Thorpe, University of York
- Léonard Turpin, Diamond Light Source
- Fatih Uzun, The University Of Oxford
- Franck Vidal, Bangor University
- Walter Villanueva, Bangor University
- Moritz Weiss, Diondo / University of Wuppertal
- Liang Yang, Cranfield University
- Miroslav Yosifov, University Of Applied Sciences Upper Austria
Details on speakers will be announced here nearer to the time of the event.
For details on speakers from previous events please see the past events page.
