Machine Learning and Dynamic Optimization for Engineers

Machine Learning and Dynamic Optimization is a 3 day short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and system optimization. It includes hands-on tutorials in data science, classification, regression, predictive control, and optimization.

Dates	Location
Jan 13-15, 2020	Seoul, South Korea (47 participants)
Mar 24-26, 2020	Manama, Bahrain with University of Bahrain
May 11-15, 2020	Salt Lake City, Utah, USA (5 day) with APCO, Inc
May 20-22, 2020	Idaho Falls, Idaho, USA
July 14-16, 2020	Houston, Texas, USA
Jan 4-8, 2021	Seoul, South Korea (73 participants)
Dec 13-16, 2021	Seoul, South Korea (16 participants)

Concepts taught in this course include machine learning, regression, classification, mathematical modeling, nonlinear programming, and advanced control methods such as model predictive control.

Day 1	Topic	Activity
9:00 AM	Overview of Course, Optimization, and GEKKO	Gekko Introduction and Machine Learning
9:30 AM	TCLab Overview	Begin Python with TCLab
10:30 AM	Break
10:45 AM	Digital Twin with Physics-based Simulation	Lab A - SISO Model or Lab B - MIMO Model
12:00 PM	Lunch Break
1:00 PM	Machine Learning Classification, Deep Learning, and LSTM Networks	TCLab Classification
2:00 PM	Data Regression for SISO/MIMO Identification	Lab C - Parameter Estimation
3:00 PM	Break
3:30 PM	Moving Horizon Estimation with Objectives/Tuning	Lab D - MHE or Lab E - Hybrid Model Estimation
4:30 PM		TCLab Incubator Project
5:30 PM	Day 1 Review	Day 1 Assessment Activity
6:00 PM	Conclude Day 1

Day 2	Topic	Activity
9:00 AM	Dynamic Control Introduction	Velocity Control
9:30 AM	Dynamic Optimization Benchmarks	Integral Objective and Economic Objective
10:30 AM	Break
10:45 AM	Crane Pendulum or Flight Control	Lab F - Linear Model Predictive Control
12:00 PM	Lunch Break
1:00 PM	Nonlinear MPC, Control Objectives/Tuning, and Orthogonal Collocation	Lab G -Nonlinear Model Predictive Control
2:00 PM	Mixed Integer Optimization	Mixed-Integer TCLab
3:00 PM	Break
3:30 PM	Multi-Objective Optimization	Lab H - Adaptive Model Predictive Control
4:30 PM	Group Projects	Project Overview
5:30 PM	Day 2 Review	Day 2 Assessment Activity
6:00 PM	Conclude Day 2

Day 3	Topic	Activity
9:00 AM	Group Project Proposals	Project Proposals
9:30 AM	Physics-based Modeling Review	Stage 1 - Develop Digital Twin Model
10:30 AM	Break
10:45 AM	Machine Learning and Time-Series Regression Review	Stage 2 - Machine learning or time-series models
12:00 PM	Lunch Break
1:00 PM	Parameter Regression Review	Stage 3 - Parameter Regression
2:00 PM	Moving Horizon Estimation Review	Stage 4 - Adaptive Model Update (MHE)
3:00 PM	Break
3:30 PM	Model Predictive Control Review	Stage 5 - Model Predictive Control
4:30 PM		Group Project Presentation Preparation
5:30 PM		Group Project Presentations (3 min each)
6:00 PM	Conclude Day 3 and Course	Certificates of Completion

Each participant has a Temperature Control Lab for hands-on exercises. Exercises are conducted in-class with additional supplementary material that can be completed after the class concludes. The objective of the 3 day short-course is to give enough background information so that researchers and practitioners can extend the methods to applications related to their field of study or industrial process.