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.

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20 reviews on LinkedIn in first 2 weeks of launch: https://tinyurl.com/JustinHodges777
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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 Amazon." - 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.