title: PhysicsGen: Can Generative Models Learn from Images to Predict Complex
    Physical Relations?
summary: 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.
paper_authors._id: 67cede23eefee37944850e24
paper_authors.name: Martin Spitznagel
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-10T12:45:56.622Z
paper_authors._id: 6628d1b6fbbb8665257e2d3a
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6628d1b6fbbb8665257e2d3a/yXU5idS0-t3X5d1SserUv.png
paper_authors.fullname: Martin Spitznagel
paper_authors.type: user
paper_authors.user: mspitzna
paper_authors._id: 67cede23eefee37944850e25
paper_authors.name: Jan Vaillant
paper_authors._id: 67cede23eefee37944850e26
paper_authors.name: Janis Keuper
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-03-14T08:58:59.226Z
paper_authors._id: 6519013e3fe8cc63b4c9b8d3
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/6519013e3fe8cc63b4c9b8d3/ShO-DM3nWhKTeg_qJ3p0P.jpeg
paper_authors.fullname: Janis Keuper
paper_authors.type: user
paper_authors.user: jkHSO
paper_title: PhysicsGen: Can Generative Models Learn from Images to Predict Complex
    Physical Relations?
paper_summary: 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.
paper_ai_keywords: image-to-image translation generative learning models image in-painting style transfer physical simulations image-pairs differential equation based simulations physical correctness