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optimizing the squeezing of the vacuum to minimize quantum noise, a prototype cryogenic interferometer, using machine learning for nonlinear feedback control, devising techniques to quell opto-mechanical
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developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and neural networks. The key challenge? Designing robust and stable numerical schemes that remain
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numerical solution a serious computational challenge. This project aims to tackle that head-on by developing adaptive numerical schemes powered by advanced nonlinear approximations—like Gaussian mixtures and
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(microelectromechanical systems) devices for X-ray optics at synchrotron radiation sources. Some background of the project is given in the publications listed below. The idea is to make highly nonlinear MEMS-based