Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems 事件

Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems arXiv:2605.25107v1 Announce Type: cross Abstract: Existing work on population dynamics inference often focuses on flows arising from vector fields that are the gradients of scalar potentials. Among all admissible flows that are compatible with the population dynamics, gradient flows are optimal in a specific sense: they minimize kinetic energy. The selection of fields based on different criteria corresp