Lost in Latent Space: An Empirical Study of Latent Diffusion Models for
  Physics Emulation
The steep computational cost of diffusion models at inference hinders their
use as fast physics emulators. In the context of image and video generation,
this computational drawback has been addressed by generating in the latent
space of an autoencoder instead of the pixel space. In this work, we
investigate whether a similar strategy can be effectively applied to the
emulation of dynamical systems and at what cost. We find that the accuracy of
latent-space emulation is surprisingly robust to a wide range of compression
rates (up to 1000x). We also show that diffusion-based emulators are
consistently more accurate than non-generative counterparts and compensate for
uncertainty in their predictions with greater diversity. Finally, we cover
practical design choices, spanning from architectures to optimizers, that we
found critical to train latent-space emulators.
686be4bc364e2ad167eb52bf
François Rozet
claimed_verified
2025-07-11T13:13:00.729Z
65a54541974f8f811f722741
https://cdn-avatars.huggingface.co/v1/production/uploads/65a54541974f8f811f722741/EAeK9QDTheyQOHw_euP34.jpeg
François Rozet
user
francois-rozet
686be4bc364e2ad167eb52c0
Ruben Ohana
claimed_verified
2025-07-09T08:51:38.138Z
630495adeb6d777a838d27b4
https://cdn-avatars.huggingface.co/v1/production/uploads/630495adeb6d777a838d27b4/qCtQ8t37vYDOsQukDznTZ.jpeg
Ruben Ohana
user
rubenohana
686be4bc364e2ad167eb52c1
Michael McCabe
686be4bc364e2ad167eb52c2
Gilles Louppe
686be4bc364e2ad167eb52c3
François Lanusse
686be4bc364e2ad167eb52c4
Shirley Ho
Lost in Latent Space: An Empirical Study of Latent Diffusion Models for
  Physics Emulation
The steep computational cost of diffusion models at inference hinders their
use as fast physics emulators. In the context of image and video generation,
this computational drawback has been addressed by generating in the latent
space of an autoencoder instead of the pixel space. In this work, we
investigate whether a similar strategy can be effectively applied to the
emulation of dynamical systems and at what cost. We find that the accuracy of
latent-space emulation is surprisingly robust to a wide range of compression
rates (up to 1000x). We also show that diffusion-based emulators are
consistently more accurate than non-generative counterparts and compensate for
uncertainty in their predictions with greater diversity. Finally, we cover
practical design choices, spanning from architectures to optimizers, that we
found critical to train latent-space emulators.
Latent-space diffusion models effectively emulate dynamical systems with high accuracy and diversity, even at high compression rates, outperforming non-generative methods.
diffusion models latent space autoencoder pixel space dynamical systems compression rates non-generative counterparts diversity architectures optimizers