Description
[LG] Branched Schrödinger Bridge Matching S Tang, Y Zhang, A Tong, P Chatterjee [Duke-NUS Medical School & Quebec AI Institute] 本文提出了分支薛定谔桥匹配（BranchSBM）框架，通过创新地将分支的广义薛定谔桥问题分解为多个可解的非平衡条件随机最优控制问题，并引入参数化的漂移场和分支特定的生长过程以及多阶段训练算法，成功实现了从单一初始分布到多个不同目标分布的动态、能量感知的分支轨迹学习，显著提升了对细胞分化、药物扰动响应等多目标发散型复杂系统建模的表达能力和准确性。https://arxiv.org/abs/2506.09007
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