I think you clarified your approach.
For this static data, I would recommend to start with a vanilla fully-connected model (using linear layers).
I assume the features have different ranges, so I would recommend to normalize them such that each feature has a zero mean and unit variance.
While mass and inertia seem to be floating point features, it seems size might be a discrete feature.
If that’s the case, you could think about using an nn.Embedding layer to transform this particular feature to a dense representation and concatenate it with the output activation for the other features.
Also, since you are working on a new architecture, I would recommend to start simple regarding the model architecture as well as the dataset. E.g. you could use with two linear layers only and use a small dataset of just 10 samples to make sure your model overfits this sample. Once this is done, you should scale the problem up.
It can get quite tricky if you start with a very deep model and the complete dataset, as you have a lot of variables to play around with.