English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

Modeling dynamical systems plays a crucial role in capturing andunderstanding complex physical phenomena. When physical models are notsufficiently accurate or hardly describable by analytical formulas, one can usegeneric function approximators such as neural networks to capture the systemdynamics directly from sensor measurements. As for now, current methods tolearn the parameters of these neural networks are highly sensitive to theinherent instability of most dynamical systems of interest, which in turnprevents the study of very long sequences. In this work, we introduce a genericand scalable method based on multiple shooting to learn latent representationsof indirectly observed dynamical systems. We achieve state-of-the-artperformances on systems observed directly from raw images. Further, wedemonstrate that our method is robust to noisy measurements and can handlecomplex dynamical systems, such as chaotic ones.

Learning Dynamical Systems from Noisy Sensor Measurements using Multiple Shooting