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Contains SEMpy's documentation and a set of examples of the library applications.
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SEMpy is a Research Tool for Mission Analysis in the Sun-Earth-Moon System
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This repository contains "experimental modular TFA" (aka xtfa), a python-implementation of the algorithms and data structures that I have developed during my thesis. For more information on the theoretical grounds, please consult my manuscript.
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A deep learning auto-regressive method is trained to learn the acoustic propagation in presence of obstacles. The method is flexible to account for both far-fields prediction (non-reflecting cases) as well as duct acoustics (non-reflecting BCs).
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Copy of the original CREME project scripts. This copy is used to learn the functioning of the original simulations and modify it.
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Reproducibility study of a multiscale CNN used for modelling PDE's
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Library for performing acoustic beamforming from signal obtained with an array of microphones.
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Effects of Boundary Conditions in Fully Convolutional Networks for Learning Spatio-temporal Dynamics
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