Approaching machine learning problems in computational fluid dynamics and computer aided engineering applications: A Monograph for Beginners
Product ID: 619659211
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This is not a traditional book. This is a monograph; a practical guide and crash- course to enable mechanical and aerospace engineers to complete machine learning projects on simulation data, from start to finish. Read More: 20 reviews on LinkedIn in first 2 weeks of launch: https://tinyurl.com/JustinHodges777 Table of Contents shown on desertcart page in web browser Who this book is for: If you are interested in ML for CFD/FEA/CAE, it's probably a fit for you. This is an abstraction of experiences into a practical guide to get CFD/CAE practitioners more comfortable in machine learning projects. After hundreds of requests for support, I felt the conviction to set aside my nights for 6 months and produce this book as a more scalable means to help. This book has a lot of (easy to understand) code (not shareable on Github). There is an abundance of resources that cover theoretical knowledge of machine learning in ‘the mainstream’, but relatively little by comparison for CAE applications (especially few that are hands-on). My hope is that the reader already has some (very minimal) theoretical knowledge when they pick this book up. There will be some explanation on the algorithms with examples (in Python), and some degree of surveying/summarizing popular ones, but the primary focus is how and what you should do to solve machine learning problems. This is what I refer to as the pipeline of steps from start to finish in a machine learning project, which seems to have a steep learning curve (my motivation for writing this book). This book will also share my recommended learning pathway for CFD/CAE engineers to develop their AI/ML skills and portfolios and is great for beginners. I am a fan of the ‘code along’ approach and take that to heart in this book. I recommend reading the book while logged into a computer where you can code. "As an AI researcher and engineer; this book must be a daily handbook for preparing a fast-changing, data-driven industry innovation for me and my collogues" - Seungkyun Hong, AI Engineering Leader @MZC, PhD in Computer Science "The book is very well structured, containing informative explanations, especially for beginners in the field. It covers the main steps of ML projects for CFD and CEA applications with some helpful examples" - Dr. Charbel Habchi, Mechanics and Thermal Hydraulics Analysis Engineer, R&D, Framatome "I believe that my long time friend and colleague Justin Hodges, PhD has made a significant contribution in this area. No wonder it is already a best seller on desertcart." - Dr. Shinjan Ghosh, Research Scientist, Siemens "This is the perfect guide to integrating AI and ML into your CAE or CFD simulations with Justin Hodges latest book, tailored for CAE engineering looking to expand their skills" - Rajat Walia, CFD Engineer (Aero Thermal), Mercedes-Benz Research and Development About the Author: While I grew up in a turbomachinery lab characterizing heat transfer, fluid mechanics, and turbulence in gas turbine secondary flow systems in graduate school, I fell in love with artificial intelligence in 2017 working on a project that combined computational fluid dynamics simulations and machine learning during an internship with the Siemens Healthineers in Princeton NJ. Ever since, I have sought to maintain my career direction (mechanical and aerospace engineering applications) but incorporate machine learning and data science as a means to augment our numerical methods in engineering. Review: A great resource to understand machine learning - The book is a great introduction to the subject of AI for engineers Review: Incredibly Grateful for Justin’s Support and Expertise - Justin took time out of his busy schedule to guide me through my project, offering invaluable insights and thoughtful advice. His deep understanding of machine learning was evident in our discussions, and his willingness to help truly made a difference. On top of that, his book is a fantastic resource—clear, insightful, and packed with practical knowledge. I highly recommend it to anyone looking to deepen their understanding of ML. Thank you, Justin!
| Best Sellers Rank | 562,406 in Books ( See Top 100 in Books ) 511 in Engineering Physics 560 in Higher Education of Engineering 674 in Higher Mathematical Education |
| Customer Reviews | 4.2 out of 5 stars 64 Reviews |
M**Y
A great resource to understand machine learning
The book is a great introduction to the subject of AI for engineers
K**E
Incredibly Grateful for Justin’s Support and Expertise
Justin took time out of his busy schedule to guide me through my project, offering invaluable insights and thoughtful advice. His deep understanding of machine learning was evident in our discussions, and his willingness to help truly made a difference. On top of that, his book is a fantastic resource—clear, insightful, and packed with practical knowledge. I highly recommend it to anyone looking to deepen their understanding of ML. Thank you, Justin!
B**S
A valuable reference
Well written, informative and concise
I**.
Review
Strongly recommended for beginners willing to learn ML in the field of CFD
A**O
Colours are not present in CFD images and plots
Colours are not present in CFD images and plots. It causes misinterpretation of the informations.
R**A
Disapointed
It is a very basic book. Shows some concepts and make some links with CFD.
A**R
At best a draft of a book! not worth the money!
There are some useful information in this book hence why I did not give it a single star. But to me it is absolutely unacceptable the author would think it is at an appropriate standard for publishing. At best this is an unedited first draft! The grammar is terrible, the writing style is hard to follow with lots of long sentences and repetitions. The explanations are vague in many places. The images are of very poor quality, very grainy, many with text that is unreadable. And I mean black and white for a book on CFD? that's laughable, especially when the author specifically mentions colors on the figure. Shame this could have been an interesting book. but I'll be sending it back!
V**N
A great book to learn about ML in CAE
As a CFD engineer, I bought this book to get a deeper understanding on the application of ML to fluid mechanics problems. This book is full of relevant examples and easy to follow, while providing the reader with a lot of references for further research. I recommend it :)
V**R
Great book for CFD practitioners wanting to start with ML
Having done my mechanical eng. thesis on CFD, and my electronic eng. thesis on neural networks, I found this book as the perfect introduction for combining both. I found the ‘Data Preparation and analysis’ and ‘Tuning your models’ chapters very useful, as it mentions tools and techniques that I had never heard of as a novice NN practitioner, like dealing with small datasets with feature creation or hyperparameter tuning. I had also never heard of Random Forest Classifiers, and now I think that they would have been a better fit for some data I worked with in the past. Also, the list of NN and CFD datasets and examples included at the end is very valuable.
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