用图像对训练生成模型预测物理关系，速度提升但准确性不足。
PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?
- 用30万张图像对训练生成模型学习物理规律
- 相比微分方程模拟，速度提升显著但物理正确性差
- 适合关注物理仿真加速与生成模型局限的研究者
生成模型在图像到图像转换方面已取得显著进展，能估计复杂图像分布间的映射关系。尽管外观类任务如图像修复和风格迁移已有深入研究，本文提出探索生成模型在物理模拟中的潜力。我们构建了一个包含30万张图像对的数据集，并为三种不同的物理模拟任务提供了基线评估。通过该基准，我们探讨两个核心问题：i) 生成模型能否从输入-输出图像对中学习复杂的物理关系？ii) 替代基于微分方程的模拟能否实现显著加速？基线评估显示，当前模型虽具备高速潜力（问题ii），但在物理正确性上仍存在明显不足（问题i）。这凸显了亟需新方法来增强生成模型的物理一致性。数据集、基线模型与评估代码见 http://www.physics-gen.org。
原文摘要 · Abstract (English)
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 http://www.physics-gen.org.
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