3D Spatially Embedded Networks: Extending the Universal Approximation Theorem

This research note offers a functional equivalence construction proof to show that with ReLU activations and bijective weight-distance kernels, an SE-FFN subgraph can replicate an arbitrary MLP edge-weight. Under these conditions, spatially embedded networks are equivalent to multilayer perceptrons, and thus share universal function approximation properties.