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Nice presentation, but the loss term for the viscosity at each points in time has the look of a weak penalty term. By that I mean this is a constraint we want the viscosity to follow. I feel that there is an equation or loss term missing to have a well-defined problem. Did you add experimental/real data to be fitted in the total loss?
Thank you everybody for putting together this easy-to-follow and helpful stuff. I always wanted to learn python (as I'm familiar with C) but was held back by the big terns associated with understanding this. This thing is really helpful.
The Google co-lab link: colab.research.google.com/github/TAMIDSpiyalong/-Computer-vision-with-pytorch-and-its-applications/blob/main/ResNet%20Dog%20Breed%20Classifer.ipynb
