Towards Learning Stochastic Population Models by Gradient Descent

arXiv:2404.07049v1 Announce Type: new
Abstract: Increasing effort is put into the development of methods for learning mechanistic models from data. This task entails not only the accurate estimation of parameters, but also a suitable model structure. Recent work on the discovery of dynamical systems formulates this problem as a linear equation system. Here, we explore several simulation-based optimization approaches, which allow much greater freedom in the objective formulation and weaker conditions on the available data. We show that even for …

abstract arxiv cs.lg data development discovery equation gradient linear parameters population stochastic systems type work