Approaching machine learning problems in computational fluid dynamics and computer aided engineering applications: A Monograph for Beginners

This book maybe the best one I’ve ever read about how to apply AI/ML techniques to computer aided engineering. Like the author said, it’s not a book about the theory of machine learning, it’s about how. The book explains the process and steps to make machine learning useful for CAE engineers. Highly recommend reading this book!

The book is good for instructors and students with limited AI/ML experience, offering guidance on applying these techniques to CFD and CAE applications. This is a code-along book that eventually makes it possible for people who have CFD data at their disposal and use it for their projects. I hope that the next edition will provide color images and more improvements.

As someone who dove into the topic of ML for CAE applications without much guidance, it’s incredible to see the summary of applications Justin puts together in this book. While my focus was more on FEA, I gained some valuable insights into the different methodologies for applying ML to these difficult problems.

Justin does a great job at introducing the different algorithms used in machine learning (ML) for computational fluid dynamics (CFD) by presenting how ML algorithms are currently being used in different areas of CFD. For those who want to dive deeper into ML for CFD he also provides invaluable references (datasets, youtube channels, and projects) on the internet as well as a recommended learning path.

I was an ML engineer knocking on the CFD area for self-study. Providing intuitive steps and code snippets, I quickly adapted my prior knowledge and ML techniques to the new area. I hope many ML engineers looking for bootstraps or quick guides consult with this book.

The book has not been edited at all. It needs editor or at least ChatGPT. The current version feels like an unedited first draft. I struggled to get through more than 10 pages due to way too lengthy sentences and missing words. Also, references are either missing or incorrect.The CFD result images too small, black and white, text is unreadable.I am sure people giving it 5 stars have not read the book. Don't spend your money on this.

The topics are very refreshing. If you are someone who already has a strong background in data science and machine learning, you can skim through the first couple of chapters and focus more on the later ones. The only drawback is that the images are not in color, which makes it difficult to understand certain sections of the book. The explanations and analogies are great, and the resources and references mentioned in this book are extremely valuable.

An excellent practical introduction to machine learning algorithms, in this case applied to CFD. Essential reading for any modeling and simulation engineer looking to broaden his or her skills for the future.Trust me (as a practicing engineer with almost 20 years of modeling and simulation experience in the medical device industry) these techniques will be vitally important to this field's future. This book is a perfect introduction to this subject.