## Machine Learning and Dynamic Optimization for Engineers

## Main.ShortCourse History

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(:cellnr:)

Dec 13-16, 2021

(:cell:)

Seoul, South Korea (16 participants)

Dec 13-16, 2021

(:cell:)

Seoul, South Korea (16 participants)

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Salt Lake City, Utah, USA (5 day) with [[http://www.apco-inc.com/upcoming-events|APCO, Inc]]

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(:cellnr:)

Jan 4-8, 2021

(:cell:)

Seoul, South Korea (73 participants)

Jan 4-8, 2021

(:cell:)

Seoul, South Korea (73 participants)

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Manama, Bahrain

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Manama, Bahrain with University of Bahrain

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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.

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(:title ~~Cyber-Physical~~ Optimization:)

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(:title Machine Learning and Dynamic Optimization for Engineers:)

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(:title ~~Machine Learning and Dynamic~~ Optimization:)

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(:title Cyber-Physical Optimization:)

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Cyber-Physical Optimization is a Machine Learning and Dynamic Optimization 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.

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(:title ~~Short Course (3 day)~~:)

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(:title Machine Learning and Dynamic Optimization:)

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Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and cyber-physical system optimization.

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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 cyber-physical system optimization.

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[[https://www.eventbrite.com/e/machine-learning-and-dynamic-optimization-tickets-89374594819?ref=elink|Salt Lake City, Utah, USA (5 day)]]

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[[https://www.eventbrite.com/e/machine-learning-and-dynamic-optimization-tickets-89374594819?ref=elink|Salt Lake City, Utah, USA (5 day)]] with [[http://www.apco-inc.com/upcoming-events|APCO, Inc]]

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Seoul, South ~~Korea~~

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Seoul, South Korea (47 participants)

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[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Gekko Introduction]]

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[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Gekko Introduction]] and [[https://playground.tensorflow.org|Machine Learning]]

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[[Main/TCLabD|Lab D - MHE]]

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[[Main/TCLabD|Lab D - MHE]] or [[Main/TCLabE|Lab E - Hybrid Model Estimation]]

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[[~~Main~~/~~TCLabE~~|~~Lab E - Hybrid Model Estimation~~]]

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[[https://github.com/APMonitor/begin_python|TCLab Incubator Project]]

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[[Main/~~ControlTypes~~|~~Crane Pendulum]] or [[Main/ModelSimulation|Flight Control~~]]

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[[Main/DynamicOptimizationBenchmarks|Dynamic Optimization Benchmarks]]

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[[Main/~~TCLabF~~|~~Lab F - Linear Model Predictive Control~~]]

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[[Main/IntegralObjective|Integral Objective]] and [[Main/EconomicDynamicOptimization|Economic Objective]]

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[[Main/~~NonlinearControl~~|~~Nonlinear MPC~~]]~~, ~~[[Main/~~ControllerObjective~~|Control~~ Objectives~~]]~~/[[Main/ControllerObjective|Tuning]], and [[Main/OrthogonalCollocation|Orthogonal Collocation]]~~

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[[Main/ControlTypes|Crane Pendulum]] or [[Main/ModelSimulation|Flight Control]]

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[[Main/~~TCLabG~~|Lab ~~G~~ -~~Nonlinear~~ Model Predictive Control]]

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[[Main/TCLabF|Lab F - Linear Model Predictive Control]]

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[[Main/~~DiscreteVariables~~|~~Mixed Integer Optimization~~]]

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[[Main/NonlinearControl|Nonlinear MPC]], [[Main/ControllerObjective|Control Objectives]]/[[Main/ControllerObjective|Tuning]], and [[Main/OrthogonalCollocation|Orthogonal Collocation]]

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[[Main/TCLabG|Lab G -Nonlinear Model Predictive Control]]

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[[Main/~~MultiObjectiveOptimization~~|~~Multi-Objective~~ Optimization]]

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[[Main/DiscreteVariables|Mixed Integer Optimization]]

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Mixed-Integer TCLab

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[[Main/MultiObjectiveOptimization|Multi-Objective Optimization]]

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[[Main/~~ProjectLab~~|~~Project Proposals~~]]

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[[Main/TCLabH|Lab H - Adaptive Model Predictive Control]]

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Group Projects

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[[Main/ProjectLab|~~Stage 1 - Develop Digital Twin Model~~]]

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[[Main/ProjectLab|Project Overview]]

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Group Project Proposals

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[[Main/ProjectLab|Project Proposals]]

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9:~~15~~ AM

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9:30 AM

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[[Main/ProjectLab|Stage 1 - Develop Digital Twin Model~~ (continued)~~]]

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[[Main/ProjectLab|Stage 1 - Develop Digital Twin Model]]

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[[https://www.eventbrite.com/e/machine-learning-and-dynamic-optimization-tickets-89374594819?ref=elink|Salt Lake City, Utah, USA (5 day)]]

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May ~~12~~-~~14~~, 2020

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May 11-15, 2020

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<a href="https://www.eventbrite.com/e/machine-learning-and-dynamic-optimization-tickets-89374594819?ref=elink" target="_blank">Salt Lake City, Utah, USA (5 day)</a>

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[[Main/EstimatorTypes|Moving Horizon Estimation]] with [[Main/EstimatorObjective|Objectives]]~~ and ~~[[Main/EstimatorTuning|Tuning]]

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[[Main/EstimatorTypes|Moving Horizon Estimation]] with [[Main/EstimatorObjective|Objectives]]/[[Main/EstimatorTuning|Tuning]]

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Machine Learning [[Main/MachineLearningClassifier|Classification]], [[Main/DeepLearning|~~Regression~~]] and [[Main/LSTMNetwork|LSTM Networks]]

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Machine Learning [[Main/MachineLearningClassifier|Classification]], [[Main/DeepLearning|Deep Learning]], and [[Main/LSTMNetwork|LSTM Networks]]

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[[Main/TCLabA|Lab A - SISO Model]]

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[[Main/TCLabA|Lab A - SISO Model]] or [[Main/TCLabB|Lab B - MIMO Model]]

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Machine Learning [[Main/MachineLearningClassifier|Classification]], [[Main/DeepLearning|Regression]]~~,~~ and [[Main/LSTMNetwork|LSTM Networks]]

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Machine Learning [[Main/MachineLearningClassifier|Classification]], [[Main/DeepLearning|Regression]] and [[Main/LSTMNetwork|LSTM Networks]]

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[[Main/~~TCLabB~~|~~Lab B - MIMO Model~~]]

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[[Main/MachineLearningClassifier|TCLab Classification]]

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[[Main/DynamicData|Data Regression]] for [[Main/DataSimulation|SISO]]/[[Main/ModelIdentification|MIMO]] Identification

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Machine Learning [[Main/MachineLearningClassifier|Classification]], [[Main/DeepLearning|Regression]], and [[Main/LSTMNetwork|LSTM Networks]]

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[[Main/NonlinearControl~~ ~~|~~ ~~Nonlinear MPC]] ~~with ~~[[Main/ControllerObjective|Control Objectives]]/[[Main/ControllerObjective|Tuning]]

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[[Main/NonlinearControl|Nonlinear MPC]], [[Main/ControllerObjective|Control Objectives]]/[[Main/ControllerObjective|Tuning]], and [[Main/OrthogonalCollocation|Orthogonal Collocation]]

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Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and cyber-physical system optimization. ~~See the [[Main~~/~~HomePage|course syllabus]] for ~~a~~ registration link to indicate interest in one of the courses.~~

to:

Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and cyber-physical system optimization.

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<button class="button"><span>Registration</span></button>

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[[Main/~~OrthogonalCollocation~~|~~Collocation Methods~~]]

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[[Main/MachineLearningClassifier|Classification]] and [[Main/OrthogonalCollocation|Collocation]] Methods

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[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|~~Linear Programming~~]]

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[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Gekko Introduction]]

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[[Main/DiscreteVariables|Mixed Integer~~]] and [[Main/MultiObjectiveOptimization|Multi-Objective~~ Optimization]]

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[[Main/DiscreteVariables|Mixed Integer Optimization]]

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[[Main/~~DeepLearning~~|~~Machine Learning~~]]~~ and [[Main/DynamicData|Data Regression]] for [[Main/DataSimulation|SISO]]/[[Main/ModelIdentification|MIMO]] Identification~~

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[[Main/MultiObjectiveOptimization|Multi-Objective Optimization]]

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Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and cyber-physical system optimization.

to:

Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and cyber-physical system optimization. See the [[Main/HomePage|course syllabus]] for a registration link to indicate interest in one of the courses.

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Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on ~~engineering design~~ and ~~real~~-~~time control applications~~.

to:

Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on machine learning and cyber-physical system optimization.

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(:title Course ~~on Cyber-Physical Optimization~~:)

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(:title Short Course (3 day):)

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Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on engineering design and real-time control applications.

(:table border=0 frame=hsides width=95%:)

(:cell width=30%:)

'''Date'''

(:cell width=70%:)

'''Location'''

(:cellnr:)

Jan 13-15, 2020

(:cell:)

Seoul, South Korea

(:cellnr:)

May 12-14, 2020

(:cell:)

Salt Lake City, Utah, USA

(:cellnr:)

June 16-18, 2020

(:cell:)

Idaho Falls, Idaho, USA

(:cellnr:)

July 14-16, 2020

(:cell:)

Houston, Texas, USA

(:tableend:)

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

(:table border=0 frame=hsides width=95%:)

(:cell width=30%:)

'''Date'''

(:cell width=70%:)

'''Location'''

(:cellnr:)

Jan 13-15, 2020

(:cell:)

Seoul, South Korea

(:cellnr:)

May 12-14, 2020

(:cell:)

Salt Lake City, Utah, USA

(:cellnr:)

June 16-18, 2020

(:cell:)

Idaho Falls, Idaho, USA

(:cellnr:)

July 14-16, 2020

(:cell:)

Houston, Texas, USA

(:tableend:)

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

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(:title~~ Short~~ Course on Cyber-Physical Optimization:)

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(:title Course on Cyber-Physical Optimization:)

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(:title Cyber-Physical Optimization:)

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(:title Short Course on Cyber-Physical Optimization:)

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[[~~https:~~/~~/apmonitor.com/pdc/index.php/Main/LinearProgramming~~|~~Linear Programming~~]]

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[[Main/DynamicControl|Velocity Control]]

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[[Main/ControlTypes|Crane Pendulum]]~~, [[Main/DynamicControl|Cruise Control]],~~ or [[Main/ModelSimulation|Flight Control]]

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[[Main/ControlTypes|Crane Pendulum]] or [[Main/ModelSimulation|Flight Control]]

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%width=~~550px~~%Attach:tclab_front.jpg

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%width=350px%Attach:tclab_front.jpg

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Each participant ~~is provided with~~ a [[https://apmonitor.com/do/index.php/Main/AdvancedTemperatureControl|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.

to:

%width=550px%Attach:tclab_front.jpg

Each participant has a [[https://apmonitor.com/do/index.php/Main/AdvancedTemperatureControl|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.

Each participant has a [[https://apmonitor.com/do/index.php/Main/AdvancedTemperatureControl|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.

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Each participant is provided with a [[https://apmonitor.com/do/index.php/Main/AdvancedTemperatureControl|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.

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'''Day ~~1~~'''

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'''Day 2'''

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'''Day 1'''

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(:title Cyber-Physical Optimization:)

(:keywords schedule, course, cyber-physical, machine learning, short course, dynamic optimization, engineering:)

(:description Short course on machine learning and dynamic optimization for scientists and engineers.:)

Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include machine learning, regression, classification, mathematical modeling, nonlinear programming, and advanced control methods such as model predictive control.

Each participant is provided with a [[https://apmonitor.com/do/index.php/Main/AdvancedTemperatureControl|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.

(:table border=0 frame=hsides width=95%:)

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'''Day 1'''

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'''Topic'''

(:cell width=40%:)

'''Activity'''

(:cellnr:)

9:00 AM

(:cell:)

Overview of [[https://youtu.be/WCTTY4baYLk|Course]], [[https://apmonitor.com/pdc/index.php/Main/OptimizationIntroduction|Optimization]], and [[https://gekko.readthedocs.io/en/latest/|GEKKO]]

(:cell:)

[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Linear Programming]]

(:cellnr:)

9:30 AM

(:cell:)

[[https://apmonitor.com/pdc/index.php/Main/ArduinoTemperatureControl|TCLab Overview]]

(:cell:)

[[https://github.com/APMonitor/begin_python|Begin Python with TCLab]]

(:cellnr:)

10:30 AM

(:cell:)

Break

(:cell:)

(:cellnr:)

10:45 AM

(:cell:)

[[Main/DynamicModeling|Digital Twin]] with [[Main/ModelFormulation|Physics-based Simulation]]

(:cell:)

[[Main/TCLabA|Lab A - SISO Model]]

(:cellnr:)

12:00 PM

(:cell:)

Lunch Break

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(:cellnr:)

1:00 PM

(:cell:)

[[Main/OrthogonalCollocation|Collocation Methods]]

(:cell:)

[[Main/TCLabB|Lab B - MIMO Model]]

(:cellnr:)

2:00 PM

(:cell:)

[[Main/DeepLearning|Machine Learning]] and [[Main/DynamicData|Data Regression]] for [[Main/DataSimulation|SISO]]/[[Main/ModelIdentification|MIMO]] Identification

(:cell:)

[[Main/TCLabC|Lab C - Parameter Estimation]]

(:cellnr:)

3:00 PM

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Break

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3:30 PM

(:cell:)

[[Main/EstimatorTypes|Moving Horizon Estimation]] with [[Main/EstimatorObjective|Objectives]] and [[Main/EstimatorTuning|Tuning]]

(:cell:)

[[Main/TCLabD|Lab D - MHE]]

(:cellnr:)

4:30 PM

(:cell:)

[[Main/DynamicOptimizationBenchmarks|Dynamic Optimization Benchmarks]]

(:cell:)

[[Main/TCLabE|Lab E - Hybrid Model Estimation]]

(:cellnr:)

5:30 PM

(:cell:)

Day 1 Review

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Day 1 Assessment Activity

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6:00 PM

(:cell:)

Conclude Day 1

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(:tableend:)

----

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'''Day 2'''

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'''Topic'''

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'''Activity'''

(:cellnr:)

9:00 AM

(:cell:)

[[Main/DynamicControl|Dynamic Control Introduction]]

(:cell:)

[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Linear Programming]]

(:cellnr:)

9:30 AM

(:cell:)

[[Main/ControlTypes|Crane Pendulum]], [[Main/DynamicControl|Cruise Control]], or [[Main/ModelSimulation|Flight Control]]

(:cell:)

[[Main/TCLabF|Lab F - Linear Model Predictive Control]]

(:cellnr:)

10:30 AM

(:cell:)

Break

(:cell:)

(:cellnr:)

10:45 AM

(:cell:)

[[Main/NonlinearControl | Nonlinear MPC]] with [[Main/ControllerObjective|Control Objectives]]/[[Main/ControllerObjective|Tuning]]

(:cell:)

[[Main/TCLabG|Lab G -Nonlinear Model Predictive Control]]

(:cellnr:)

12:00 PM

(:cell:)

Lunch Break

(:cell:)

(:cellnr:)

1:00 PM

(:cell:)

[[Main/DiscreteVariables|Mixed Integer]] and [[Main/MultiObjectiveOptimization|Multi-Objective Optimization]]

(:cell:)

Mixed-Integer TCLab

(:cellnr:)

2:00 PM

(:cell:)

[[Main/DeepLearning|Machine Learning]] and [[Main/DynamicData|Data Regression]] for [[Main/DataSimulation|SISO]]/[[Main/ModelIdentification|MIMO]] Identification

(:cell:)

[[Main/TCLabH|Lab H - Adaptive Model Predictive Control]]

(:cellnr:)

3:00 PM

(:cell:)

Break

(:cell:)

(:cellnr:)

3:30 PM

(:cell:)

Create Project Proposals and Evaluate Resources

(:cell:)

[[Main/ProjectLab|Project Proposals]]

(:cellnr:)

4:30 PM

(:cell:)

Determine Application Scope

(:cell:)

[[Main/ProjectLab|Stage 1 - Develop Digital Twin Model]]

(:cellnr:)

5:30 PM

(:cell:)

Day 2 Review

(:cell:)

Day 2 Assessment Activity

(:cellnr:)

6:00 PM

(:cell:)

Conclude Day 2

(:cell:)

(:tableend:)

----

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'''Day 3'''

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'''Topic'''

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'''Activity'''

(:cellnr:)

9:00 AM

(:cell:)

Overview of Group Project

(:cell:)

(:cellnr:)

9:15 AM

(:cell:)

Physics-based Modeling Review

(:cell:)

[[Main/ProjectLab|Stage 1 - Develop Digital Twin Model (continued)]]

(:cellnr:)

10:30 AM

(:cell:)

Break

(:cell:)

(:cellnr:)

10:45 AM

(:cell:)

Machine Learning and Time-Series Regression Review

(:cell:)

[[Main/ProjectLab|Stage 2 - Machine learning or time-series models]]

(:cellnr:)

12:00 PM

(:cell:)

Lunch Break

(:cell:)

(:cellnr:)

1:00 PM

(:cell:)

Parameter Regression Review

(:cell:)

[[Main/ProjectLab|Stage 3 - Parameter Regression]]

(:cellnr:)

2:00 PM

(:cell:)

Moving Horizon Estimation Review

(:cell:)

[[Main/ProjectLab|Stage 4 - Adaptive Model Update (MHE)]]

(:cellnr:)

3:00 PM

(:cell:)

Break

(:cell:)

(:cellnr:)

3:30 PM

(:cell:)

Model Predictive Control Review

(:cell:)

[[Main/ProjectLab|Stage 5 - Model Predictive Control]]

(:cellnr:)

4:30 PM

(:cell:)

(:cell:)

Group Project Presentation Preparation

(:cellnr:)

5:30 PM

(:cell:)

(:cell:)

Group Project Presentations (3 min each)

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6:00 PM

(:cell:)

Conclude Day 3 and Course

(:cell:)

Certificates of Completion

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(:keywords schedule, course, cyber-physical, machine learning, short course, dynamic optimization, engineering:)

(:description Short course on machine learning and dynamic optimization for scientists and engineers.:)

Machine Learning and Dynamic Optimization is a short course on the theory and applications of numerical methods for solution of time-varying systems with a focus on engineering design and real-time control applications. Concepts taught in this course include machine learning, regression, classification, mathematical modeling, nonlinear programming, and advanced control methods such as model predictive control.

Each participant is provided with a [[https://apmonitor.com/do/index.php/Main/AdvancedTemperatureControl|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.

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'''Day 1'''

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'''Topic'''

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'''Activity'''

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9:00 AM

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Overview of [[https://youtu.be/WCTTY4baYLk|Course]], [[https://apmonitor.com/pdc/index.php/Main/OptimizationIntroduction|Optimization]], and [[https://gekko.readthedocs.io/en/latest/|GEKKO]]

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[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Linear Programming]]

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9:30 AM

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[[https://apmonitor.com/pdc/index.php/Main/ArduinoTemperatureControl|TCLab Overview]]

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[[https://github.com/APMonitor/begin_python|Begin Python with TCLab]]

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10:30 AM

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Break

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10:45 AM

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[[Main/DynamicModeling|Digital Twin]] with [[Main/ModelFormulation|Physics-based Simulation]]

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[[Main/TCLabA|Lab A - SISO Model]]

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12:00 PM

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Lunch Break

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1:00 PM

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[[Main/OrthogonalCollocation|Collocation Methods]]

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[[Main/TCLabB|Lab B - MIMO Model]]

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2:00 PM

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[[Main/DeepLearning|Machine Learning]] and [[Main/DynamicData|Data Regression]] for [[Main/DataSimulation|SISO]]/[[Main/ModelIdentification|MIMO]] Identification

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[[Main/TCLabC|Lab C - Parameter Estimation]]

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3:00 PM

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Break

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3:30 PM

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[[Main/EstimatorTypes|Moving Horizon Estimation]] with [[Main/EstimatorObjective|Objectives]] and [[Main/EstimatorTuning|Tuning]]

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[[Main/TCLabD|Lab D - MHE]]

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4:30 PM

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[[Main/DynamicOptimizationBenchmarks|Dynamic Optimization Benchmarks]]

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[[Main/TCLabE|Lab E - Hybrid Model Estimation]]

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5:30 PM

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Day 1 Review

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Day 1 Assessment Activity

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6:00 PM

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Conclude Day 1

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'''Day 2'''

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'''Topic'''

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'''Activity'''

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9:00 AM

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[[Main/DynamicControl|Dynamic Control Introduction]]

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[[https://apmonitor.com/pdc/index.php/Main/LinearProgramming|Linear Programming]]

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9:30 AM

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[[Main/ControlTypes|Crane Pendulum]], [[Main/DynamicControl|Cruise Control]], or [[Main/ModelSimulation|Flight Control]]

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[[Main/TCLabF|Lab F - Linear Model Predictive Control]]

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10:30 AM

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Break

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10:45 AM

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[[Main/NonlinearControl | Nonlinear MPC]] with [[Main/ControllerObjective|Control Objectives]]/[[Main/ControllerObjective|Tuning]]

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[[Main/TCLabG|Lab G -Nonlinear Model Predictive Control]]

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12:00 PM

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Lunch Break

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1:00 PM

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[[Main/DiscreteVariables|Mixed Integer]] and [[Main/MultiObjectiveOptimization|Multi-Objective Optimization]]

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Mixed-Integer TCLab

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2:00 PM

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[[Main/DeepLearning|Machine Learning]] and [[Main/DynamicData|Data Regression]] for [[Main/DataSimulation|SISO]]/[[Main/ModelIdentification|MIMO]] Identification

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[[Main/TCLabH|Lab H - Adaptive Model Predictive Control]]

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3:00 PM

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Break

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3:30 PM

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Create Project Proposals and Evaluate Resources

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[[Main/ProjectLab|Project Proposals]]

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4:30 PM

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Determine Application Scope

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[[Main/ProjectLab|Stage 1 - Develop Digital Twin Model]]

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5:30 PM

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Day 2 Review

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Day 2 Assessment Activity

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6:00 PM

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Conclude Day 2

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'''Day 3'''

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'''Topic'''

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'''Activity'''

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9:00 AM

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Overview of Group Project

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9:15 AM

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Physics-based Modeling Review

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[[Main/ProjectLab|Stage 1 - Develop Digital Twin Model (continued)]]

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10:30 AM

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Break

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10:45 AM

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Machine Learning and Time-Series Regression Review

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[[Main/ProjectLab|Stage 2 - Machine learning or time-series models]]

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12:00 PM

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Lunch Break

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1:00 PM

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Parameter Regression Review

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[[Main/ProjectLab|Stage 3 - Parameter Regression]]

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2:00 PM

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Moving Horizon Estimation Review

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[[Main/ProjectLab|Stage 4 - Adaptive Model Update (MHE)]]

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3:00 PM

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Break

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3:30 PM

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Model Predictive Control Review

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[[Main/ProjectLab|Stage 5 - Model Predictive Control]]

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4:30 PM

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Group Project Presentation Preparation

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5:30 PM

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Group Project Presentations (3 min each)

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6:00 PM

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Conclude Day 3 and Course

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Certificates of Completion

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