Data-driven Model Learning of Dynamic Systems

Like in 2023 and 2024, and 2025 the 2026-edition will be held in hybrid mode. Participants can thus attend the Spring School in person (on the campus of Ecole Centrale de Lyon) or virtually.

For this purpose, we will make use of the brandnew lecture hall of Ecole Centrale de Lyon that is completely dedicated to hybrid teaching. This lecture hall is located in the SKYLAB of Ecole Centrale.

Like in the previous editions, the 2026-edition will welcome an international guest lecturer.

Dynamical models play a key role in many branches of science. In engineering they have a paramount role in model-based simulation, health monitoring, control and optimization. The accuracy of the models is often crucial to their subsequent use in model-based operations.

Data-driven modeling (system identification) and statistical parameter estimation are established fields for determining mathematical models of dynamical systems on the basis of measurement data from dedicated experiments.

The 5-days Spring School aims at covering the fundamentals of data-driven modeling approaches (ranging from parameter estimation algorithms (PEM and ETFE) and experiment design to model validation) as well as more advanced topics. In this year edition, these advanced topics will pertain to closed-loop identification, to subspace identification, to gray-box identification and to deep learning.

The school consists of a series of lectures and of (computer) exercise sessions.

For the computer exercices, participants should have a computer with one of the latest versions of Matlab (version R2018a at least) installed. The Matlab System Identification Toolbox must be available.

The school is mainly aimed at an audience of PhD students in control engineering (or related fields), but the course is also open to any other persons interested in the topic of data-based modeling.

The course is eligible for scientific doctoral modules (+/- 20 hours). A certificate of attendance will be delivered to all participants at the end of the course.

Basics of linear system identification

Lectures on Monday 20 April (afternoon) and on Tuesday 21 April (all day)

Exercises on Wednesday 22 April (morning)

Lecturer: Xavier Bombois, CNRS Research Director, Laboratoire Ampère, Ecole Centrale de Lyon

• Theme 1: Introduction;concepts; identification cycle
• Theme 2: Parametric (prediction error) identification methods: prediction criterion and model structures, linear and pseudo-linear regressions, conditions on data, statistical and asymptotic properties, model set selection and model validation
• Theme 3: Non-parametric identification (ETFE)
• Theme 4: Experiment design.

Closed-loop identification

Lecture on Wednesday 22 April (beginning of the afternoon)

Lecturer: Xavier Bombois, CNRS Research Director, Laboratoire Ampère, Ecole Centrale de Lyon

• Theme 1: Direct closed-loop method
• Theme 2: Indirect closed-loop methods

Gray-box and black-box state-space model learning

Lectures on Wednesday 22 April (end of the afternoon) and Thursday 23 April (morning)

Lecturer: Guillaume Mercère, Associate Professor, Laboratoire LIAS, Université de Poitiers

• Theme 1: Black-box model learning: subspace state-space model identification
• Theme 2: Gray-box model learning: nonlinear least-squares state-space model identification
• Theme 3: From black-box to gray-box models

Deep Learning for System Identification

Lectures on Thursday 23 April (afternoon) and on Friday 24 April (morning)

Lecturer: Marco Forgione, Senior Researcher, IDSIA, Lugano, Switzerland

• Theme 1: Introduction to Deep Learning
• Theme 2: Feedforward neural networks for static regression
• Theme 3: Recurrent neural networks for dynamical systems modeling

Fees for participants attending in person and requiring accommodation

• 550 euros (VAT included) for PhD students
  
• 730 euros (VAT included) for other participants
  

These fees include the participation at the Spring School, the accommodation in the Résidence Paul Comparat from April 20 till April 25 (five nights) and the lunches on on April 21, 22, 23 and 24.

Fees for participants attending in person (without accommodation)

• 350 euros (VAT included) for PhD students
  
• 530 euros (VAT included) for other participants
  

These fees include the participation at the Spring School and the lunches on April 21, 22, 23 and 24.

Fees for participants attending virtually

• 150 euros (VAT included) for PhD students
  
• 250 euros (VAT included) for other participants
  

These fees include the participation at the Spring School in a fully virtual fashion.