PhysicsGen: Can Generative Models Learn from Images to Predict Complex Physical Relations?
This work addresses the problem of accelerating physical simulations for researchers and engineers, but it is incremental as it highlights limitations and the need for new methods.
The paper investigates whether generative models can learn complex physical relations from image pairs, finding that while they offer high speedups over differential equation simulations, they have strong limitations in physical correctness.
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.