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.

翻译：数据驱动的复杂物理系统建模正受到模拟与机器学习领域的日益关注。由于大多数物理模拟基于微分方程系统的计算密集型迭代实现，用学习得到的一步推理模型进行（部分）替代，有望在广泛的应用领域实现显著加速。在此背景下，我们提出一种新颖的基准，用于评估一步生成学习模型在速度和物理正确性方面的表现。我们的城市声音传播基准建立在物理上复杂且实际相关、但直观易懂的任务之上，即模拟城市环境中声源的二维波传播。我们提供了一个包含10万个样本的数据集，每个样本由从OpenStreetMap提取的真实二维建筑地图对、参数化声源以及给定场景下模拟的真实声音传播数据组成。该数据集提供了四种不同复杂度的模拟任务，涉及反射、衍射和声源变化。对常见生成式U-Net、GAN和扩散模型的初步基线评估表明，尽管这些模型在简单情况下能够很好地建模声音传播，但对高阶方程所表示子系统的近似却系统性失败。数据集信息、下载说明及源代码均可在我们的网站上获取：https://www.urban-sound-data.org。