I'm trying to solve biochemistry problems (think protein folding) with DNNs. Are there 2D / 3D coordinate systems that are particularly well suited for deep neural networks (DNNs) to process?
For example, if we were training a DNN to predict gravitational attraction between two objects, cartesian coordinates would presumably do worse than polar coordinates, because the key value to calculate gravitation attraction is distance, a value directly expressed in polar coordinates but would require a DNN to learn the Pythagorean rule when using cartesian coordinates.
As another example, arguably the positional embeddings based on sine and cosine used in early Transformers was an interesting "coordinate system" in 1D.
Another answer might be not to worry about it - that DNNs will figure it out no matter what coordinate system (within reason) you pick.