MATLAB | Design Optimization with MATLAB @MATLAB | Uploaded October 2024 | Updated October 2024, 1 week ago.
Engineers use design optimization tools to automate finding the best design parameters while satisfying project requirements and to evaluate trade-offs among competing designs. Using these tools results in faster design iterations and allows evaluating a larger number of parameters and alternative designs compared with manual approaches.
We will show how to use apps and functions in Optimization Toolbox and Global Optimization Toolbox to define and solve design optimization problems. Optimization can be applied to design models that are either analytic or black-box including those built with machine learning and simulations. We will use examples from different engineering domains to demonstrate these capabilities.
Highlights
- Defining objectives, constraints and design variables
- Interactively creating and solving optimization problems with an app
- Choosing the best solver for your problem
- Setting options to improve results
- Using parallel computing to accelerate design studies
Chapters:
00:00 Introduction to design optimization
10:16 Multistage rocket design optimization example
15:50 Current-carrying cables design optimization example
28:26 Electrified powertrain gear ratios design optimization example
41:42 Tips for selecting optimization tools
45:21 Key takeaways
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Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
Engineers use design optimization tools to automate finding the best design parameters while satisfying project requirements and to evaluate trade-offs among competing designs. Using these tools results in faster design iterations and allows evaluating a larger number of parameters and alternative designs compared with manual approaches.
We will show how to use apps and functions in Optimization Toolbox and Global Optimization Toolbox to define and solve design optimization problems. Optimization can be applied to design models that are either analytic or black-box including those built with machine learning and simulations. We will use examples from different engineering domains to demonstrate these capabilities.
Highlights
- Defining objectives, constraints and design variables
- Interactively creating and solving optimization problems with an app
- Choosing the best solver for your problem
- Setting options to improve results
- Using parallel computing to accelerate design studies
Chapters:
00:00 Introduction to design optimization
10:16 Multistage rocket design optimization example
15:50 Current-carrying cables design optimization example
28:26 Electrified powertrain gear ratios design optimization example
41:42 Tips for selecting optimization tools
45:21 Key takeaways
--------------------------------------------------------------------------------------------------------
Get a free product trial: goo.gl/ZHFb5u
Learn more about MATLAB: goo.gl/8QV7ZZ
Learn more about Simulink: goo.gl/nqnbLe
See what's new in MATLAB and Simulink: goo.gl/pgGtod
© 2024 The MathWorks, Inc. MATLAB and Simulink are registered trademarks of The MathWorks, Inc.
See mathworks.com/trademarks for a list of additional trademarks. Other product or brand names may be trademarks or registered trademarks of their respective holders.
