# Physics-informed generative neural networks for RF propagation prediction with application to indoor body perception

arXiv: https://arxiv.org/abs/2405.02131 · HTML: https://mlsanity.com/paper/2405.02131/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 3 May 2024.

## Builds on (most cited first, 4 of 4)

- [Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Models](https://mlsanity.com/paper/2103.04922/) (2021)
- [Electromagnetic Models for Passive Detection and Localization of Multiple Bodies](https://mlsanity.com/paper/2104.07354/) (2021)
- [A physics-informed generative model for passive radio-frequency sensing](https://mlsanity.com/paper/2310.04173/) (2023)
- [An EM Body Model for Device-Free Localization with Multiple Antenna Receivers: A First Study](https://mlsanity.com/paper/2405.09558/) (2024)

## Built on by (most cited first, 1 of 1)

- [Radio Propagation Modelling: To Differentiate or To Deep Learn, That Is The Question](https://mlsanity.com/paper/2509.19337/) (2025)

## Abstract (arXiv)

Electromagnetic (EM) body models designed to predict Radio-Frequency (RF) propagation are time-consuming methods which prevent their adoption in strict real-time computational imaging problems, such as human body localization and sensing. Physics-informed Generative Neural Network (GNN) models have been recently proposed to reproduce EM effects, namely to simulate or reconstruct missing data or samples by incorporating relevant EM principles and constraints. The paper discusses a Variational Auto-Encoder (VAE) model which is trained to reproduce the effects of human motions on the EM field and incorporate EM body diffraction principles. Proposed physics-informed generative neural network models are verified against both classical diffraction-based EM tools and full-wave EM body simulations.