Jul 27 – 31, 2020
Virtual event
Europe/Berlin timezone

Deep learning of multibody minimal coordinates for estimation

Jul 29, 2020, 7:30 PM
Poster Posters 2


Andrea Angeli (KU Leuven, Flanders Make)


Multibody systems are the state-of-the-art tool to model complex mechanical mechanisms. However, they typically include redundant coordinates plus constraints, leading to differential algebraic equations for the dynamics which require dedicated integration schemes and control/estimation algorithms.
In my work, autoencoder neural networks are combined with the multibody physics information. In this way, the autoencoder does not only perform a dimensionality reduction of the original coordinates but can be used for a model order reduction obtaining a reduced-order model where the dynamics is expressed with ordinary differential equations and standard estimation algorithms can be used.
This permits to combine the physics-informed neural network with measurements in order to estimate unknown parameters or inputs in the system, for instance with an extended Kalman filtering scheme.

Primary author

Andrea Angeli (KU Leuven, Flanders Make)


Prof. Frank Naets (KU Leuven, Flanders Make) Prof. Wim Desmet (KU Leuven, Flanders Make)

Presentation materials