# Graph Convolutional Neural Networks for Body Force Prediction

arXiv: https://arxiv.org/abs/2012.02232 · HTML: https://mlsanity.com/paper/2012.02232/

## Key facts

- Cited by 16 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 16 of 16)

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- [MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics](https://mlsanity.com/paper/2212.01428/) (2022)
- [Learning to simulate partially known spatio-temporal dynamics with trainable difference operators](https://mlsanity.com/paper/2307.14395/) (2023)
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## Abstract (arXiv)

Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model to perform inference on fields defined on an unstructured mesh, using a Graph Convolutional Neural Network (GCNN) is presented. The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements. The network can infer from field samples at different resolutions, and is invariant to the order in which the measurements within each sample are presented. The GCNN method, using inductive convolutional layers and adaptive pooling, is able to predict this quantity with a validation R2 above 0.98, and a Normalized Mean Squared Error below 0.01, without relying on spatial structure.