Price: $27.50
(as of Dec 24,2024 08:22:19 UTC – Details)
ASIN : B0CZF4YN31
Publisher : Independently published (March 26, 2024)
Language : English
Paperback : 168 pages
ISBN-13 : 979-8878702317
Item Weight : 11 ounces
Dimensions : 6 x 0.38 x 9 inches
Customers say
Customers find the book’s introduction well-organized and describing important themes. They find it easy to follow examples and consider it an essential resource for modeling and simulation engineers. However, some readers have issues with the readability – some find the illustrations and figure labels difficult to read, while others praise the engaging content and cover-to-cover reading experience.
AI-generated from the text of customer reviews
Machine learning has revolutionized many fields in recent years, including computational fluid dynamics and computer aided engineering. This powerful tool has the potential to greatly enhance the accuracy and efficiency of simulations in these areas, but approaching machine learning problems in these domains can be daunting for beginners.
In this monograph, we will provide a comprehensive overview of how machine learning can be applied to computational fluid dynamics and computer aided engineering applications. We will cover the basic concepts of machine learning, such as supervised and unsupervised learning, as well as more advanced topics like deep learning and reinforcement learning.
We will also discuss the various challenges and considerations that come with using machine learning in these fields, such as data preprocessing, model selection, and validation. Additionally, we will provide practical guidance on how to approach machine learning problems in computational fluid dynamics and computer aided engineering, including tips for selecting the right algorithms and techniques for your specific application.
Whether you are a student looking to learn more about the intersection of machine learning and engineering, or a practitioner seeking to enhance your simulation capabilities, this monograph will provide you with the foundational knowledge and practical insights needed to successfully apply machine learning to computational fluid dynamics and computer aided engineering applications.
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