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.

Dynamic Optimization

Machine Learning and Dynamic Optimization for Engineers

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Dynamic Optimization