Estimation of 2D pressure and cavitation fields from sparse pseudo-pressure sensor point data using super-resolution machine learning

The two-dimensional pressure field around a hydrofoil is estimated from the pressure values of sparse sensors flush-mounted on the hydrofoil.The sparse Kids Bodysuit data is expanded to pressure field data using a combination of multi-layer perceptron (MLP) and super-resolution convolutional neural network (SRCNN) techniques, where MLP is employed

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Data assimilation for fault slip monitoring and short-term prediction of spatio-temporal evolution of slow slip events: application to the 2010 long-term slow slip event in the Bungo Channel, Japan

Abstract Monitoring and predicting fault slip behaviors in subduction zones is essential for understanding earthquake cycles and assessing future earthquake potential.We developed a data assimilation method for fault slip monitoring and the short-term prediction of slow slip events, and applied to the 2010 Bungo Channel slow slip event in southwest

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