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 anonymous 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仿真与机器学习领域日益增长的关注。由于大多数物理仿真基于计算密集型微分方程组的迭代求解，用学习型单步推理模型（部分）替代这些仿真，有望在众多应用领域实现显著加速。为此，我们提出一个用于评估单步生成学习模型在速度与物理正确性方面表现的新型基准测试。我们构建的城市声传播基准以物理复杂、实际相关且直观易理解的二维城市环境中声源波动传播建模任务为基础。我们提供了一个包含10万个样本的数据集，每个样本由真实城市建筑地图（来自OpenStreetMap）的二维图像对、参数化声源以及该场景的仿真地面真实声传播数据组成。数据集提供四种复杂度递增的仿真任务，涉及反射、衍射及声源变化等物理效应。基于通用生成U-Net、生成对抗网络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较好模拟声传播，但对高阶方程表征子系统的近似存在系统性失败。数据集信息、下载说明及源代码可通过匿名网站获取：https://www.urban-sound-data.org。