Abstract
The image-to-image translation abilities of generative learning models have recently made significant progress in the estimation of complex (steered) mappings between image distributions. While appearance based tasks like image in-painting or style transfer have been studied at length, we propose to investigate the potential of generative models in the context of physical simulations. Providing a dataset of 300k image-pairs and baseline evaluations for three different physical simulation tasks, we propose a benchmark to investigate the following research questions: i) are generative models able to learn complex physical relations from input-output image pairs? ii) what speedups can be achieved by replacing differential equation based simulations? While baseline evaluations of different current models show the potential for high speedups (ii), these results also show strong limitations toward the physical correctness (i). This underlines the need for new methods to enforce physical correctness. Data, baseline models and evaluation code this http URL.
Abstract (translated)
最近，生成式学习模型在估计图像分布之间的复杂（偏转）映射方面取得了显著进展。虽然已经对基于外观的任务（如图像修复或风格转换）进行了深入研究，但我们提议探讨生成式模型在物理模拟中的潜力。为此，我们提供了一个包含30万张图像配对的数据集以及针对三种不同物理仿真任务的基准评估。我们的目标是通过以下研究问题来探究这一领域： i) 生成式模型是否能够从输入-输出图像对中学习复杂的物理关系？ ii) 替换基于微分方程的模拟可以实现多大的加速效果？ 虽然当前各种模型的基础性评估显示了实现高加速率（ii）的巨大潜力，但这些结果也表明在确保物理正确性方面存在严重局限（i）。这强调了需要开发新的方法来强制执行物理正确性。数据集、基准模型和评估代码可在此网址获取：[请在这里插入链接]。
URL
https://arxiv.org/abs/2503.05333
