Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting
Armand Jordana,
Justin Carpentier,
Ludovic Righetti
Abstract:Modeling dynamical systems plays a crucial role in capturing and understanding complex physical phenomena. When physical models are not sufficiently accurate or hardly describable by analytical formulas, one can use generic function approximators such as neural networks to capture the system dynamics directly from sensor measurements. As for now, current methods to learn the parameters of these neural networks are highly sensitive to the inherent instability of most dynamical systems of interest, which in turn… Show more
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