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Field
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. Combined, they reinforce each other and are the driving force behind the technology we all use in our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely
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-temporal machine learning method development, including: generative models for grid-based and particle-based spatio-temporal data; controlled generation methods for data assimilation; and graph-based multi
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our daily lives. Technology such as the electricity grid, which our faculty is helping to make completely sustainable and future-proof. At the same time, we are developing the chips and sensors
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for preventing and mitigating antagonistic threats in developing an integrated solution for digital twins from different domains (such as production, power grids, public infrastructure and spaces, logistics chains
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a set of use cases with energy community projects ranging from smart grid experimentation and energy sharing in rural regions in the Netherlands to developing an energy community in Amsterdam
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Vulnerability Detection of Smart Grids with a Specific Focus on Generative Adversarial Networks (GAN) Attacks Primary supervisors: Professor Damminda Alahakoon & Dr Shalinka Jayatilleke Other supervisors