Engineering
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Electronics and computer science
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Mathematical sciences
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Physics and astronomy
Machine learning models for subgrid scales in turbulent reacting flows
This PhD project advances deep learning for turbulence modeling in combustion. Using CNNs and GANs, it tackles challenges in data demands and generalization. The goal is to develop predictive models for hydrogen-based and carbon-neutral fuels, guiding sustainable energy design. Key tasks include model optimization, integration, and Exascale scalability.