PhysicsGen: Can Generative Models Learn from Images to Predict Complex
  Physical Relations?
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.
67cede23eefee37944850e24
Martin Spitznagel
claimed_verified
2025-03-10T12:45:56.622Z
6628d1b6fbbb8665257e2d3a
https://cdn-avatars.huggingface.co/v1/production/uploads/6628d1b6fbbb8665257e2d3a/yXU5idS0-t3X5d1SserUv.png
Martin Spitznagel
user
mspitzna
67cede23eefee37944850e25
Jan Vaillant
67cede23eefee37944850e26
Janis Keuper
claimed_verified
2025-03-14T08:58:59.226Z
6519013e3fe8cc63b4c9b8d3
https://cdn-avatars.huggingface.co/v1/production/uploads/6519013e3fe8cc63b4c9b8d3/ShO-DM3nWhKTeg_qJ3p0P.jpeg
Janis Keuper
user
jkHSO
PhysicsGen: Can Generative Models Learn from Images to Predict Complex
  Physical Relations?
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.
Generative models are evaluated for learning physical simulation tasks, demonstrating potential speedups and limitations in physical correctness.
image-to-image translation generative learning models physical simulations image-pairs differential equation based simulations