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M. Spera","Samir Darouich","Timo Klenk","Niels Hansen" ], "authors": [ {"first" : "Maximiliam", "last" : "Fleck"}, {"first" : "Marcelle B. M.", "last" : "Spera"}, {"first" : "Samir", "last" : "Darouich"}, {"first" : "Timo", "last" : "Klenk"}, {"first" : "Niels", "last" : "Hansen"} ], "volume": "4","number": "12","pages": "3578-3587","abstract": "Data-driven approaches used to predict thermophysical properties benefit from physical constraints because the extrapolation behavior can be improved and the amount of training data be reduced. In the present work, the well-established entropy scaling approach is incorporated into a neural network architecture to predict the shear viscosity of a diverse set of pure fluids over a large temperature and pressure range. Instead of imposing a particular form of the reference entropy and reference shear viscosity, these properties are learned. 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