PhysicsGen - Can Generative Models Learn from Images to Predict Complex Physical Relations?

This dataset comprises 300,000 pairs of images designed for the advancement of generative model applications in physical simulations. Each pair consists of an input image and its corresponding output image that represents a physical simulation. The dataset aims to facilitate research into whether generative models can effectively learn and reproduce complex physical dynamics from visual data, potentially replacing traditional differential equation-based methods with significant computational speedups. Data, baseline models and evaluation code: https://www.physics-gen.org本数据集包含30万组图像对，旨在推动生成式模型在物理模拟领域的应用发展。每一组图像对均由一张输入图像与一张代表物理模拟结果的对应输出图像构成。本数据集旨在助力相关研究，探索生成式模型能否从视觉数据中有效学习并复现复杂物理动态，进而有望替代传统基于微分方程的方法，实现计算效率的显著提升。 数据集、基准模型与评估代码可访问：https://www.physics-gen.org