Learning dynamics from partial observations with structured neural ODEs. (arXiv:2205.12550v1 [eess.SY])

Identifying dynamical systems from experimental data is a notably difficult
task. Prior knowledge generally helps, but the extent of this knowledge varies
with the application, and customized models are often needed. We propose a
flexible framework to incorporate a broad spectrum of physical insight into
neural ODE-based system identification, giving physical interpretability to the
resulting latent space. This insight is either enforced through hard
constraints in the optimization problem or added in its cost function. In order
to link the …

arxiv dynamics learning