Dutycycle symmetry
Opened this issue · 3 comments
Testing 3C94 material with triangular waves under MagNet AI showed that losses are not symmetric in duty cycle. Why?
The model is fully data driven and we try to avoid putting prior-human knowledge in developing the neural network model. Asymmetries exist in our experimental data, excitation waveform, measurement accuracy, for different duty ratios. As a result, we cannot guarantee full symmetry in the training data, and the model picks up the asymmetry.
One can develop neural network models that will produce "pure" symmetry data by reasonable hypothesis, but that is beyond our purpose. A data driven model cannot ensure full "symmetry" in experiments and in training, and the material may have intrinsic "asymmetry" due to memory effect (existing theory cannot completely rule out this possibility).
These asymmetry is more likely to happen for low core loss data, and low core loss materials. This is a fact comes from measurement raw data and the model inherits it.
Yes agreed! A model that can leverage both the strengths of data driven models and physical understanding would perform the best. We leave it to the community to find the best way to combine them.
For now, we will keep our model 100% data driven to perhaps highlight / warn the weakness of a pure data driven model - the biases / inaccuracy in data will directly transfer to biases / inaccuracy in models.