  We present a novel machine-learning (ML) approach (EM-GANSim) for real-time
electromagnetic (EM) propagation that is used for wireless communication
simulation in 3D indoor environments. Our approach uses a modified conditional
Generative Adversarial Network (GAN) that incorporates encoded geometry and
transmitter location while adhering to the electromagnetic propagation theory.
The overall physically-inspired learning is able to predict the power
distribution in 3D scenes, which is represented using heatmaps. Our overall
accuracy is comparable to ray tracing-based EM simulation, as evidenced by
lower mean squared error values. Furthermore, our GAN-based method drastically
reduces the computation time, achieving a 5X speedup on complex benchmarks. In
practice, it can compute the signal strength in a few milliseconds on any
location in 3D indoor environments. We also present a large dataset of 3D
models and EM ray tracing-simulated heatmaps. To the best of our knowledge,
EM-GANSim is the first real-time algorithm for EM simulation in complex 3D
indoor environments. We plan to release the code and the dataset.
