arXiv cs.AI· Junda Ying, Yuxuan Wang, Bowen Yang, Peijie Zhou, Lei Zhang·· 11 小时前AI 评分18
SUDO：面向通用增长惩罚的无仿真非平衡动态最优传输框架
Simulation-free Unbalanced Dynamic Optimal Transport with General Growth Penalty
AI 导读
研究者提出 SUDO，一个面向通用非二次凸增长惩罚的非平衡动态最优传输（UDOT）无仿真框架，通过学习条件路径与传输代价、求解诱导的半耦合问题并结合非平衡流匹配实现求解。在 WFR 基准上，SUDO 精度与解析解驱动算法相当，计算速度快于基于仿真的方法。它还原生支持可编码增殖主导先验的非对称惩罚，在合成数据与单细胞数据上给出更合理的轨迹与增长估计。
正文 · 原文
Abstract:Inferring cellular dynamics from unpaired single-cell snapshots requires modeling both state transitions and population growth or death. Unbalanced dynamic optimal transport (UDOT) addresses this by penalizing growth along transport paths, making the choice of growth penalty a key way to encode biological priors on proliferation and apoptosis. However, existing UDOT solvers either rely on computationally expensive NeuralODE simulations or depend on analytical solutions of conditional paths, restricting their efficiency solely to quadratic penalties, i.e. Wasserstein-Fisher-Rao (WFR) geodesics. To enable an efficient UDOT solver for general growth penalties, we first show that concave growth penalties lead to degenerate solutions where growth and transport are separated. We then introduce \textbf{S}imulation-free \textbf{U}nbalanced \textbf{D}ynamic \textbf{O}ptimal transport (SUDO), a simulation-free framework for UDOT with general non-quadratic convex growth penalties. SUDO learns the conditional paths and transport costs, solves the induced semi-coupling problem, and subsequently leverages unbalanced flow matching to achieve a simulation-free solution. On WFR benchmarks, SUDO matches the accuracy of efficient, analytical solution-driven algorithms while outperforming simulation-based methods in computational speed. Beyond WFR, SUDO supports asymmetric penalties that encode proliferation-dominant priors and produce more plausible trajectories and growth estimates on synthetic and single-cell datasets.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Quantitative Methods (q-bio.QM) |
| Cite as: | arXiv:2609.04710 [cs.LG] |
| (or arXiv:2609.04710v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2609.04710 arXiv-issued DOI via DataCite |
Submission history
From: Junda Ying [view email]
[v1]
Fri, 4 Sep 2026 04:30:05 UTC (7,099 KB)
[v2]
Fri, 30 Oct 2026 15:35:05 UTC (7,099 KB)
来源：arXiv cs.AI · arxiv.org