Participants need to register in advance using the registration link:
- Note that this workshop runs in parallel with this Machine Learning and Process Simulation workshops. Only one of them can be selected
- Workshop Date: Sunday 18th of June.
- Workshop Starting Time: 10:00 AM.
- Participants Limit: 60.
- Registrations Deadline: Monday 12 June 2023.
- Workshop Coordinator: Prof Carl Laid, Carnegie Mellon University
Hands-on with OMLT:
The Optimization and Machine Learning Toolkit
Presented by: Carl D. Laird, Chemical Engineering Department, CMU
OMLT is developed in collaboration with the Misener and Tsay groups (Imperial College), Sandia National Laboratories, and CMU.
Ceccon, F., Jalving, J., Haddad, J., Thebelt, A., Tsay, C., Laird, C. D., & Misener, R. (2022). OMLT: Optimization & machine learning toolkit. The Journal of Machine Learning Research, 23(1), 15829-15836.
Machine learning (ML) models are being increasingly used as surrogates for complex processes within science and engineering applications. While most ML software is focused on training and forward evaluation of ML models, there is a need to solve the “inverse” problem – that is, given an objective
and constraints that depend on the outputs, can we solve for the best values of the inputs. The Optimization and Machine Learning Toolkit (OMLT) integrates trained ML models (e.g., neural networks, decision trees) within the optimization package, Pyomo, supporting the use of ML surrogates within optimization applications.
In this hands-on tutorial, I will discuss the OMLT package and applications of optimization with machine learning surrogates. Using Jupyter notebooks and other Python exercises, we will provide hands-on experience with the tools. We will also demonstrate the integration of OMLT and the IDAES framework.