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diagnosis of gas turbines. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components
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. The project focuses on developing an integrated approach that combines machine learning techniques with physics-based models to estimate the health of various system components. The aim is that fault diagnosis
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of electrodes and catalysts for low temperature fuel cells, electrochemical hydrogen generation, batteries, as well as heavy metal removal from aqueous solutions. By designing well controlled model systems in
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engineers, and researchers in materials science and nanotechnology. We are developing the superconducting quantum devices, control circuits, firmware, and methods required to make the quantum computer a
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generation, batteries, as well as heavy metal removal from aqueous solutions. By designing well controlled model systems in combination with powerful analysis and characterization techniques, improved
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, physics-informed control, and digital twin technologies. Project description The project focuses on the development of robotic methods for plant health monitoring that combine robot–plant interaction with
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