is it a good idea to take the derivative or integral of some features and add them as new features in machine learning?
While performing feature engineering , transforming the existing features is recommended step instead of adding transformed values of the existing feature into the dataset.
Since, if we add these kind of data elements they will be highly correlated with each other and it effects the model performance.
If you have less features and cannot create a model, ask for additional data.