# Mathematical Foundations of Geometric Deep Learning

arXiv: https://arxiv.org/abs/2508.02723 · HTML: https://mlsanity.com/paper/2508.02723/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 14 references that are in the map.
- First posted on arXiv 1 Aug 2025.

## Builds on (most cited first, 1 of 1)

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges](https://mlsanity.com/paper/2104.13478/) (2021)

## Built on by (most cited first, 5 of 5)

- [Deep Learning in Classical and Quantum Physics](https://mlsanity.com/paper/2508.10666/) (2025)
- [Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity](https://mlsanity.com/paper/2605.07097/) (2026)
- [VDW-GNNs: Vector diffusion wavelets for geometric graph neural networks](https://mlsanity.com/paper/2510.01022/) (2025)
- [A graph neural network based chemical mechanism reduction method for combustion applications](https://mlsanity.com/paper/2603.22318/) (2026)
- [GUIDED Network-Agnostic Feature Initialization for Spatial Transferability in GNN-based Models](https://mlsanity.com/paper/2607.19270/) (2026)

## Abstract (arXiv)

We review the key mathematical concepts necessary for studying Geometric Deep Learning.