Probability-Flow ODE in Infinite-Dimensional Function Spaces
Machine Learning
2025-03-14 v1 Machine Learning
Abstract
Recent advances in infinite-dimensional diffusion models have demonstrated their effectiveness and scalability in function generation tasks where the underlying structure is inherently infinite-dimensional. To accelerate inference in such models, we derive, for the first time, an analog of the probability-flow ODE (PF-ODE) in infinite-dimensional function spaces. Leveraging this newly formulated PF-ODE, we reduce the number of function evaluations while maintaining sample quality in function generation tasks, including applications to PDEs.
Keywords
Cite
@article{arxiv.2503.10219,
title = {Probability-Flow ODE in Infinite-Dimensional Function Spaces},
author = {Kunwoo Na and Junghyun Lee and Se-Young Yun and Sungbin Lim},
journal= {arXiv preprint arXiv:2503.10219},
year = {2025}
}
Comments
26 pages, 8 figures. Accepted to the ICLR 2025 DeLTa Workshop