4 papers
Riemannian Diffusion Models on General Manifolds via Physics-Informed Neural Networks
Gyeonghoon Ko, Juho Lee
Riemannian diffusion models generalize score-based generative modeling to manifold-supported data via stochastic diffusion equations on the manifold. However, training requires sam…
Active Learning with Selective Time-Step Acquisition for PDEs
Yegon Kim, Hyunsu Kim, Gyeonghoon Ko +1
Accurately solving partial differential equations (PDEs) is critical to understanding complex scientific and engineering phenomena, yet traditional numerical solvers are computatio…
Permutation-Symmetrized Diffusion for Unconditional Molecular Generation
Gyeonghoon Ko, Juho Lee
Permutation invariance is fundamental in molecular point-cloud generation, yet most diffusion models enforce it indirectly via permutation-equivariant networks on an ordered space.…
Learning Infinitesimal Generators of Continuous Symmetries from Data
Gyeonghoon Ko, Hyunsu Kim, Juho Lee
Exploiting symmetry inherent in data can significantly improve the sample efficiency of a learning procedure and the generalization of learned models. When data clearly reveals und…