A Neural JKO Scheme for Hellinger-Kantorovich Gradient Flows via Monge-Growth Pairs
We develop a mesh-free neural JKO scheme for advection-reaction-diffusion equations with a gradient-flow structure in the Hellinger-Kantorovich (HK) geometry of unbalanced optimal transport. Each update is parametrized by a spatial map and a mass-changing factor, allowing spatial redistribution and local mass creation or loss to be treated jointly within a single variational step. Their cone action bounds the squared HK distance from above, yielding a sufficient condition for discrete energy dissipation through comparison with the identity pair. Minimizing the pair objective over all admissible pairs recovers the exact JKO minimum when the source and a minimizer have positive densities. W...
| Authors | Geuntaek Seo, Cheolhyeong Kim, Hwijae Son et al. |
|---|---|
| Primary category | math.NA |
| Published | 6 October 2026 |
| Last updated at source | 6 October 2026 |
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