A graph convolutional autoencoder approach to model order reduction for parametrized PDEs
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École Polytechnique Fédérale de Lausanne, Computer Science Department2000
In a society which produces and consumes an ever increasing amount of information, methods which can make sense out of al1 this data become of crucial importance. Machine learning tries to develop models which can make the information load accessible. Thre ...
