  Data-driven modeling of complex physical systems is receiving a growing
amount of attention in the simulation and machine learning communities. Since
most physical simulations are based on compute-intensive, iterative
implementations of differential equation systems, a (partial) replacement with
learned, 1-step inference models has the potential for significant speedups in
a wide range of application areas. In this context, we present a novel
benchmark for the evaluation of 1-step generative learning models in terms of
speed and physical correctness. Our Urban Sound Propagation benchmark is based
on the physically complex and practically relevant, yet intuitively easy to
grasp task of modeling the 2d propagation of waves from a sound source in an
urban environment. We provide a dataset with 100k samples, where each sample
consists of pairs of real 2d building maps drawn from OpenStreetmap, a
parameterized sound source, and a simulated ground truth sound propagation for
the given scene. The dataset provides four different simulation tasks with
increasing complexity regarding reflection, diffraction and source variance. A
first baseline evaluation of common generative U-Net, GAN and Diffusion models
shows, that while these models are very well capable of modeling sound
propagations in simple cases, the approximation of sub-systems represented by
higher order equations systematically fails. Information about the dataset,
download instructions and source codes are provided on our website:
https://www.urban-sound-data.org.
