Machine Learning and Dynamic Optimization for Engineers

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(:cellnr:) Dec 13-16, 2021 (:cell:) Seoul, South Korea (16 participants)

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Salt Lake City, Utah, USA (5 day) with APCO, Inc

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Salt Lake City, Utah, USA (5 day) with APCO, Inc

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(:cellnr:) Jan 4-8, 2021 (:cell:) Seoul, South Korea (73 participants)

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June 16-18, 2020

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May 20-22, 2020

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Manama, Bahrain

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Manama, Bahrain with University of Bahrain

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Bahrain

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Manama, Bahrain

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(:cellnr:) Mar 24-26, 2020 (:cell:) Bahrain

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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.

to:

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.

January 27, 2020, at 06:00 PM by 12.45.189.171 -

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(:title Cyber-Physical Optimization:)

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

January 26, 2020, at 02:11 PM by 50.249.67.137 -

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(:title Machine Learning and Dynamic Optimization:)

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(:title Cyber-Physical 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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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.

January 26, 2020, at 02:09 PM by 50.249.67.137 -

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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.

to:

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.

January 25, 2020, at 03:59 PM by 50.249.67.137 -

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Salt Lake City, Utah, USA (5 day)

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Salt Lake City, Utah, USA (5 day) with APCO, Inc

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Seoul, South Korea

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Seoul, South Korea (47 participants)

January 25, 2020, at 03:39 PM by 50.249.67.137 -

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January 11, 2020, at 07:39 PM by 147.46.252.163 -

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Gekko Introduction

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Gekko Introduction and Machine Learning

January 11, 2020, at 07:22 PM by 147.46.252.163 -

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Lab D - MHE

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Lab D - MHE or Lab E - Hybrid Model Estimation

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Dynamic Optimization Benchmarks

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Lab E - Hybrid Model Estimation

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TCLab Incubator Project

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Crane Pendulum or Flight Control

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Dynamic Optimization Benchmarks

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Lab F - Linear Model Predictive Control

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Integral Objective and Economic Objective

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Nonlinear MPC, Control Objectives/Tuning, and Orthogonal Collocation

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Crane Pendulum or Flight Control

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Lab G -Nonlinear Model Predictive Control

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Lab F - Linear Model Predictive Control

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Mixed Integer Optimization

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Nonlinear MPC, Control Objectives/Tuning, and Orthogonal Collocation

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Mixed-Integer TCLab

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Lab G -Nonlinear Model Predictive Control

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Multi-Objective Optimization

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Mixed Integer Optimization

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Lab H - Adaptive Model Predictive Control

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Mixed-Integer TCLab

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Create Project Proposals and Evaluate Resources

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Multi-Objective Optimization

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Project Proposals

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Lab H - Adaptive Model Predictive Control

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Determine Application Scope

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Group Projects

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Stage 1 - Develop Digital Twin Model

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Project Overview

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Overview of Group Project

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Group Project Proposals

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Project Proposals

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9:15 AM

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9:30 AM

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Stage 1 - Develop Digital Twin Model (continued)

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Stage 1 - Develop Digital Twin Model

January 11, 2020, at 08:21 AM by 147.46.252.162 -

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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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Salt Lake City, Utah, USA

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December 28, 2019, at 10:44 PM by 136.36.211.159 -

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Moving Horizon Estimation with Objectives and Tuning

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Moving Horizon Estimation with Objectives/Tuning

December 28, 2019, at 10:43 PM by 136.36.211.159 -

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Machine Learning Classification, Regression and LSTM Networks

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Machine Learning Classification, Deep Learning, and LSTM Networks

December 28, 2019, at 10:15 PM by 136.36.211.159 -

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Lab A - SISO Model

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Lab A - SISO Model or Lab B - MIMO Model

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Machine Learning Classification, Regression, and LSTM Networks

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Machine Learning Classification, Regression and LSTM Networks

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Lab B - MIMO Model

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TCLab Classification

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Machine Learning and Data Regression for SISO/MIMO Identification

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Data Regression for SISO/MIMO Identification

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Classification and Collocation Methods

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Machine Learning Classification, Regression, and LSTM Networks

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Nonlinear MPC with Control Objectives/Tuning

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Nonlinear MPC, Control Objectives/Tuning, and Orthogonal Collocation

December 26, 2019, at 02:59 PM by 136.36.211.159 -

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December 26, 2019, at 02:58 PM by 136.36.211.159 -

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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 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.

(:html:) <a href='https://apmonitor.com/do/index.php/Main/InfoSheet'> <button class="button"><span>Registration</span></button> </a> (:htmlend:)

December 20, 2019, at 06:00 PM by 136.36.211.159 -

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Collocation Methods

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Classification and Collocation Methods

December 02, 2019, at 03:33 PM by 136.36.211.159 -

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Linear Programming

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Gekko Introduction

December 02, 2019, at 01:21 AM by 174.148.195.243 -

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Mixed Integer and Multi-Objective Optimization

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Mixed Integer Optimization

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Machine Learning and Data Regression for SISO/MIMO Identification

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Multi-Objective Optimization

November 27, 2019, at 04:22 PM by 136.36.211.159 -

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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 course syllabus for a registration link to indicate interest in one of the courses.

November 27, 2019, at 03:56 PM by 136.36.211.159 -

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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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Date

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Dates

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November 27, 2019, at 03:52 PM by 136.36.211.159 -

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

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 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.

November 25, 2019, at 07:11 PM by 136.36.211.159 -

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

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(:title Course on Cyber-Physical Optimization:)

November 25, 2019, at 07:10 PM by 136.36.211.159 -

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(:title Cyber-Physical Optimization:)

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

November 25, 2019, at 05:10 PM by 136.36.211.159 -

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Linear Programming

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Velocity Control

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Crane Pendulum, Cruise Control, or Flight Control

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Crane Pendulum or Flight Control

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to:

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Each participant is provided with 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.

to:

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.

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Each participant is provided with 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.

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Each participant is provided with 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.

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Day 1

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Day 2

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Day 1

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Day 3

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(:cell width=10%:) Day 2 (:cell width=50%:)

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(:cell width=15%:) Day 1 (:cell width=45%:)

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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 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%:)

(:cell width=10%:) Day 1 (:cell width=50%:) Topic (:cell width=40%:) Activity

(:cellnr:) 9:00 AM (:cell:) Overview of Course, Optimization, and GEKKO (:cell:) Linear Programming

(:cellnr:) 9:30 AM (:cell:) TCLab Overview (:cell:) Begin Python with TCLab

(:cellnr:) 10:30 AM (:cell:) Break (:cell:)

(:cellnr:) 10:45 AM (:cell:) Digital Twin with Physics-based Simulation (:cell:) Lab A - SISO Model

(:cellnr:) 12:00 PM (:cell:) Lunch Break (:cell:)

(:cellnr:) 1:00 PM (:cell:) Collocation Methods (:cell:) Lab B - MIMO Model

(:cellnr:) 2:00 PM (:cell:) Machine Learning and Data Regression for SISO/MIMO Identification (:cell:) Lab C - Parameter Estimation

(:cellnr:) 3:00 PM (:cell:) Break (:cell:)

(:cellnr:) 3:30 PM (:cell:) Moving Horizon Estimation with Objectives and Tuning (:cell:) Lab D - MHE

(:cellnr:) 4:30 PM (:cell:) Dynamic Optimization Benchmarks (:cell:) Lab E - Hybrid Model Estimation

(:cellnr:) 5:30 PM (:cell:) Day 1 Review (:cell:) Day 1 Assessment Activity

(:cellnr:) 6:00 PM (:cell:) Conclude Day 1 (:cell:)

(:tableend:)

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

(:cell width=10%:) Day 2 (:cell width=50%:) Topic (:cell width=40%:) Activity

(:cellnr:) 9:00 AM (:cell:) Dynamic Control Introduction (:cell:) Linear Programming

(:cellnr:) 9:30 AM (:cell:) Crane Pendulum, Cruise Control, or Flight Control (:cell:) Lab F - Linear Model Predictive Control

(:cellnr:) 10:30 AM (:cell:) Break (:cell:)

(:cellnr:) 10:45 AM (:cell:) Nonlinear MPC with Control Objectives/Tuning (:cell:) Lab G -Nonlinear Model Predictive Control

(:cellnr:) 12:00 PM (:cell:) Lunch Break (:cell:)

(:cellnr:) 1:00 PM (:cell:) Mixed Integer and Multi-Objective Optimization (:cell:) Mixed-Integer TCLab

(:cellnr:) 2:00 PM (:cell:) Machine Learning and Data Regression for SISO/MIMO Identification (:cell:) 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:) Project Proposals

(:cellnr:) 4:30 PM (:cell:) Determine Application Scope (:cell:) 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:)

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

(:cell width=10%:) Day 3 (:cell width=50%:) Topic (:cell width=40%:) Activity

(:cellnr:) 9:00 AM (:cell:) Overview of Group Project (:cell:)

(:cellnr:) 9:15 AM (:cell:) Physics-based Modeling Review (:cell:) 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:) 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:) Stage 3 - Parameter Regression

(:cellnr:) 2:00 PM (:cell:) Moving Horizon Estimation Review (:cell:) Stage 4 - Adaptive Model Update (MHE)

(:cellnr:) 3:00 PM (:cell:) Break (:cell:)

(:cellnr:) 3:30 PM (:cell:) Model Predictive Control Review (:cell:) 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)

(:cellnr:) 6:00 PM (:cell:) Conclude Day 3 and Course (:cell:) Certificates of Completion

(:tableend:)

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Dynamic Optimization

Machine Learning and Dynamic Optimization for Engineers

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