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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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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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of these cold clouds, and their precipitation, can be controlled by the loadings of solid aerosol particles that initiate ice particles. There is controversy about the identity of the chemical species of aerosol
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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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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