Research project

Data-Augmented Modeling Of Constitutive Laws for Engineering Systems

Duration
September 2021 - August 2025
Partners
Control Systems
Dynamic Networks: Data-Driven Modeling and Control
Machine Learning for Modelling and Control
Electrical Engineering
Autonomous Motion Control Lab
ICMS Affiliated
EAISI
Processing and Performance
High Tech Systems Center
Group Anderson
Project Manager
Roland Tóth

The modeling of complex engineering systems is highly challenging. Physics-based models require a cautious application of constitutive assumptions, whereas data-based models require vast amounts of data. DAMOCLES (Data-Augmented Modeling Of Constitutive Laws for Engineering Systems projects) targets a breakthrough in the constitutive modeling of such systems in different physical domains by developing a unified multi-tool framework that combines the favorable characteristics of physics-based and data-based approaches. As one part of the DAMOCLES project, this research aims to learn constitutive laws in a multi-physics setting using an ANN-based Hamiltonian model learning framework.

Existing highly flexible data-driven model learning methods can capture a wide range of system behaviors, but lack good generalization properties, and the resulting models are difficult to interpret due to their black-box character. Hence, this project aims to use an ANN-based Hamiltonian model learning framework to obtain interpretable, robust, and generalizable models of the underlying system dynamics.

Collaborative Partners

  • Eindhoven University of Technology
  • EAISI

Our Partners

Researchers involved in this project

Full Professor

Roland Toth

Associate Professor

Maarten Schoukens

Doctoral Candidate

Sarvin Moradi