Approaching Machine Learning Problems in Computational Fluid Dynamics and Computer-Aided Engineering Applications: A Monograph for Beginners

As a practical guide and crash-course, this book enables mechanical and aerospace engineers to complete machine learning projects on simulation data, from start to finish. (A “machine learning pipeline” for simulation projects). It fills a gap in educational resources that exist for learning and adopting machine learning.

Adoption of Artificial Intelligence (AI) and Machine Learning (ML) into industry is more feasible through dedicated resources, rather than sprawling content across many platforms/websites/books and being forced to collate this information themselves, as a beginner in AI/ML. The book is directly applicable to the engineering simulation community, with still benefit to experimentalists, since the MAE concepts we build from in the book are the same, as well as templated approach for applying AI/ML to engineering projects.

Who this book is for:

If you are interested in ML for Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA) or Computer-Aided Engineering (CAE), this book is a good 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 those who want to become more ML-savvy in their industry and academic projects.

This book contains 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.

To Order

To order this book, click on the link corresponding to the country in which you reside.

Note: If your country is not listed, use the following genius link: https://geni.us/JaXp

As this is an international book, and shipping logistics can sometimes be a challenge, you can contact the author with any shipping issues/fees, and he will help make the book available in your local country. Contact Dr. Justin Hodges at: justins.latent.space@gmail.com

Author: Dr. Justin Hodges

Dr. Hodges is Senior AI/ML Technical Specialist, Simcenter Product Management for Siemens. In addition, he serves as a Rev-Sim volunteer helping to guide our AI/ML initiative.

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.

What Readers are Saying

While the book targets beginners in AI/ML, it has gotten overwhelmingly positive feedback from people with AI/ML expertise.

“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 longtime 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

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