Program of our Summer School on Machine Learning for Process and Systems Engineering

Program of our Summer School on Machine Learning for Process and Systems Engineering

Monday, September 27, 2021: Theory of Machine Learning

8:15 - 8:30  
Opening of the Summer School
Kai Sundmacher, Max-Planck-Institute, Magdeburg, Germany

8:30 - 9:30   
"title" tba
Sanaz Mostaghim, Otto-von-Guericke University Magdeburg, Germany

9:30 - 9:50
Coffee break

9:50 - 11:10
"title" tba
Jens Bremer, Max-Planck-Institute Magdeburg, Germany &
Edgar Sanchez-Medina
, Otto-von-Guericke-University Magdeburg, Germany

11:10 - 11:30
Coffee break

11:30 - 12:50
" title" tba
Anton Savchenko, Otto-von-Guericke University, Magdeburg, Germany &
Rolf Findeisen, Technische Universität Darmstadt, Germany/ Otto-von-Guericke University Magdeburg, Germany

12:50 - 14:00
Lunch break

14:00 - 15:30
"Learning Dynamical Models from Data"
Pavan Goyal & Peter Benner, Max-Planck-Institute Magdeburg, Germany

15:30 - 16:00
Coffee break

16:00 - 17:30
"title" tba
Thomas Richter & Christian Lessig, Otto-von-Guericke University Magdeburg, Germany

 

Tuesday, September 28, 2021: Implementation and Algorithms in Machine Learning

9:00 - 10:30
"Introduction to AI in MATLAB"
Julia Hoerner, Mathworks, Cambridge, United Kingdom &
Philip Laserstein, Mathworks, Ismaning, Germany

10:30 - 11:00
Coffee break

11:00-12:00
"Deep Dive into AI with MaTLAB"
Julia Hoerner, Mathworks, Cambridge, United Kingdom &
Philip Laserstein, Mathworks, Ismaning, Germany

12:00 - 14:00
Lunch break

14:00 - 15:30
"Data-Driven Optimization in Dynamic Systems: Solutions, Structure, and Control
Julius Martensen & Sebastian Sager, Otto-von-Guericke University, Magdeburg, Germany

16:00 - 22:00
Social event incl. conference dinner
(unfortunately for IMPRS members & speakers only)

 

Wednesday, September 29, 2021: Machine Learning Applications in Process and Systems Engineering

9:00 - 10:30
" title" tba
Rolf Findeisen, Otto-von-Guericke University Magdeburg, Germany/ Technische Universität Darmstadt, Germany

10:30 - 11:00
Coffee break

11:00 - 12:30
" title" tba
Stefan Palkovitz, Rheinisch Westfälische Technische Hochschule Aachen, Germany

12:30 - 14:00
Lunch break

14:00 - 15:30
"Machine Learning and Hybrid Modeling in Process Engineering"
Moritz von Stosch, datahow, Dübendorf, Switzerland

15:30
Closing remarks

..............................................................................................

Abstracts of the lectures

"Introduction to population balance modeling of particulate processes"
Sanaz Mostaghim
, Otto-von-Guericke University Magdeburg, Germany  
Abstract follows soon.

"title" tba
Jens Bremer
, Max-Planck-Institute Magdeburg, Germany &   
Edgar Sanchez Medina, Otto-von-Guericke University Magdeburg, Germany 
Abstract follows soon.

"title tba"
Anton Savchenko, Otto-von-Guericke University, Magdeburg, Germany &
Rolf Findeisen, Technische Universität Darmstadt, Germany/ Otto-von-Guericke University Magdeburg, Germany
Abstract follows soon.

"Learning Dynamical Models from Data"
Pavan Goyal &
Peter Benner, Max-Planck-Institute Magdeburg, Germany
Abstract follows soon.

"title" tba
Thomas Richter & Christian Lessig
, Otto-von-Guericke University Magdeburg, Germany
Abstract follows soon.

" Introduction to AI in MATLAB"
Julia Hoerner, Mathworks, Cambridge, United Kingdom &
Philip Laserstein, Mathworks, Ismaning, Germany

  • Definition of AI
  • AI workflow
  • Fundamentals of Machine Learning and Deep Learning (incl 2 examples)
  • Summary

"Deep into AI with MATLAB "
Julia Hoerner, Mathworks, Cambridge, United Kingdom &
Philip Laserstein, Mathworks, Ismaning, Germany

  • Quick intro
  • Hyperparameter tuning
  • Customisation of networks
  • Interoperability with other frameworks
  • Summary

"Data-Driven Optimization in Dynamic Systems: Solutions, Structure, and Control
Julius Martensen & Sebastian Sager, Otto-von-Guericke University, Magdeburg, Germany
Machine Learning (ML) has proven to be a valuable tool for data analysis, control, and many other areas. Modeling and optimization are at the core of extracting patterns, finding anomalies, and generating additional scientific insight from experimental data. In this lecture, we will present applications of ML in connection to the fields of mixed-integer optimization, neural networks, and system estimation. The use of optimal control techniques within the modelling and training of ML will be explored and explained by showcasing the classification of cardiac arrhythmia. Furthermore, we will highlight some of the latest work in the field of symbolic regression and how to apply it to generate the equations of motion from data.

"title" tba
Rolf Findeisen, Technische Universität Darmstadt, Germany/ Otto-von-Guericke University Magdeburg, Germany 
Abstract follows soon.

"title" tba
Stefan Palkovitz, RWTH Aachen, Germany
Abstract follows soon.

"Machine Learning and Hybrid Modeling in Process Engineering"
Moritz von Stosch, datahow, Dübendorf, Switzerland
The advent of new machine learning algorithms and more powerful computers has rendered the solution of problems possible that were for long considered not to be machine solvable. Also in the process industries these tools have found increasing attention and application. However, machine learning tools require high quantities of qualitative data, the generation of which being typically expensive. In contrast to other industries, fundamental knowledge in form of mathematical equations is available in the process industries. By combining the fundamental knowledge with machine learning tools in so called hybrid models, the data requirements can be significantly reduced and the extrapolation properties of the hybrid models remain valid beyond experimentally tested combinations. This lecture provides an introduction into machine learning and hybrid modeling and their application in the industries.

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