AI projects
AI for Net Zero is integrating artificial intelligence (AI) into three projects focused on wind, road transport and aviation to accelerate decarbonization by increasing their efficiency through optimisation and employing digital twin modelling.
Real-time Digital Twins
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The optimisation of multi-physics problems, such as turbulent flows, are difficult computational problems. Understanding these systems better will require real-time modelling to predict their dynamics. This work package will generalise offline scientific machine learning to real-time modelling. This will enable autonomous real-time decision-making, improving the energy efficiency of real engineering systems. |
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Principle Investigator |
Anastasia Borovykh is a Lecturer at the Department of Mathematics, Imperial College London. |
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Principle Investigator |
Luca Magri is the Professor of Scientific Machine Learning, Department of Aeronautics, Imperial College London. |
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Andrea Nova Research Associate |
Andrea Nova is a Research Associate at the Department of Aeronautics at Imperial College. |
Efficient use of hardware for HPC/AI workflows
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The rapid growth in computing resources is increasing the industry’s carbon footprint. This impact can be reduced with efficient use of AI workflows, minimising wasted computing resources. This package will investigate this using EPCC’s exotic hardware, AI and data infrastructure, different cloud platforms and Tier 1-2 systems. |
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Principle Investigator |
Michèle Weiland is the Professor and Director of Research |
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Co-lead researcher |
Sylvain Laizet is the Professor in Computational Fluid Mechanics in the |
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Applications Consultant |
Eleanor Broadway is a Postdoctoral Researcher at The University of Edinburgh. |
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Applications Consultant |
Joseph Lee is a Postdoctoral Researcher at The University of Edinburgh. |
