Models and datasets for "Learning effective good variables from physical data"
Models and datasets for "Learning effective good variables from physical data"
Trained models and datasets related to the GitHub repository https://github.com/giuliobarl/GoodPhysVariables and to our publication Dittus.xlsx, Gnielinski.xlsx, and Newton.xlsx are the original datasets with no noise; Dittus Noise.xlsx, Gnielinski Noise.xlsx, and Newton Noise.xlsx are the datasets to which gaussian noise has been added, and that are used to train the DNN models; dittus_model.tf.zip contains the trained DNN model for the Dittus-Boelter correlation; gnielinski_model.tf.zip contains the trained DNN model for the Gnielinski correlation; newton_model.tf.zip contains the trained DNN model for Newton's law of universal gravitation.
Physical Property Invariance, Feature grouping, Primitive Variable Analysis, Machine Learning in Physics
Physical Property Invariance, Feature grouping, Primitive Variable Analysis, Machine Learning in Physics
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