DDE-Find: Learning Delay Differential Equations from Data

arXiv:2405.02661v1 Announce Type: new
Abstract: Delay Differential Equations (DDEs) are a class of differential equations that can model diverse scientific phenomena. However, identifying the parameters, especially the time delay, that make a DDE's predictions match experimental results can be challenging. We introduce DDE-Find, a data-driven framework for learning a DDE's parameters, time delay, and initial condition function. DDE-Find uses an adjoint-based approach to efficiently compute the gradient of a loss function with respect to the model parameters. We motivate and …

abstract arxiv class cs.lg data data-driven delay differential diverse experimental framework however match parameters predictions results scientific type