AI for Design and Optimization
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This comprehensive course explores how artificial intelligence and machine learning technologies are revolutionizing engineering design and optimization processes. Students will gain essential knowledge of AI fundamentals and their practical applications in mechanical engineering contexts. The curriculum begins with core concepts of machine learning algorithms and neural networks, establishing a solid foundation for understanding how these technologies can be leveraged in design workflows. The second module focuses on generative design techniques, including Generative Adversarial Networks (GANs), that can dramatically expand the solution space and inspire innovative approaches to engineering problems. The final module delves into AI-driven optimization algorithms such as topology optimization, with real-world case studies in thermal insulation and battery fast-charging optimization. Throughout the course, practical programming exercises in Python help students develop hands-on skills. By completing this course, engineers will be able to discern appropriate applications of AI, interpret model outputs accurately, and effectively integrate these powerful tools into their design and optimization processes.
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What you'll learn
Apply machine learning algorithms to engineering design problems
Implement deep neural networks for complex engineering analyses
Understand and utilize generative design principles
Develop generative adversarial networks for creating design solutions
Apply AI-driven optimization algorithms to engineering challenges
Implement topology optimization techniques enhanced with machine learning
Skills you'll gain
This course includes:
1.5 Hours PreRecorded video
3 assignments
Access on Mobile, Tablet, Desktop
FullTime access
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There are 3 modules in this course
This course provides a comprehensive introduction to artificial intelligence applications in engineering design and optimization. The curriculum is structured in three main modules that build progressively from fundamental concepts to advanced applications. Students begin by exploring essential AI and machine learning concepts, including various algorithms, deep learning, and neural networks, with specific focus on their relevance to engineering design challenges. The second module introduces generative design techniques that leverage AI to expand the solution space and enhance creativity in engineering problems. Students learn about generative adversarial networks (GANs) and how these technologies can be applied to create novel design solutions. The final module focuses on AI-driven optimization algorithms, including topology optimization, with practical case studies in thermal insulation and battery fast-charging optimization. Throughout the course, programming exercises in Python provide hands-on experience implementing these concepts. By combining theoretical knowledge with practical applications, the course equips engineers with the skills needed to effectively integrate AI into their design and optimization workflows.
Introduction to Key Concepts and Fundamentals of Artificial Intelligence (AI) and Machine Learning (ML)
Module 1 · 4 Hours to complete
Generative Design Techniques
Module 2 · 1 Hours to complete
AI-Driven Optimization Algorithms
Module 3 · 4 Hours to complete
Fee Structure
Instructor
Professor of Mechanical Engineering
Wei Lu is a Professor in the Department of Mechanical Engineering at the University of Michigan - Ann Arbor. He earned his B.S. from Tsinghua University and his Ph.D. from Princeton University. Prof. Lu specializes in applying machine learning to solve critical challenges in mechanical engineering and energy applications. With over 180 publications in high-impact journals and 200 presentations at prestigious institutions like Harvard, MIT, and Stanford, he is a recognized leader in his field. Prof. Lu's research spans a wide range of topics, integrating artificial intelligence with engineering solutions. His works have been featured in prominent journals such as Nature Communications, Applied Energy, and Journal of Power Sources, showcasing his contributions to both specialized and interdisciplinary audiences.
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