Capturing Actionable Dynamics with Structured Latent Ordinary Differential Equations. (arXiv:2202.12932v2 [stat.ML] UPDATED)

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 …

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