Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations.

ABSTRACT: End-to-end learning of dynamical systems with black-box models, such as neural ordinary differential equations (ODEs), provides a flexible framework for learning dynamics from data without prescribing a mathematical model for the dynamics. Unfortunately, this flexibility comes at the cost of understanding the dynamical system, for which ODEs are used ubiquitously. Further, experimental data are collected under various conditions (inputs), such as treatments, or grouped in some way, such as part of sub-populations. Understanding the effects of these system inputs on system outputs is crucial to have any meaningful model of a dynamical system. To that end, we propose a structured latent ODE model that explicitly captures system input variations within its latent representation. Buildi

SUBMITTER: Chapfuwa P 

PROVIDER: S-EPMC12441811 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature