Smart Proxy Modeling: Artificial Intelligence and Machine Learning in Numerical Simulation

Numerical simulation models are used in all engineering disciplines to model physical phenomena and optimize behavior. Smart Proxy Models simplify the use of complex numerical simulations by replicating them with high accuracy and can be run on a laptop within minutes. This book focuses on Smart Proxy Modeling and provides readers with the essential details on how to develop them using Artificial Intelligence and Machine Learning, as well as their real-world applications.

Format: Hardback
Length: 190 pages
Publication date: 18 October 2022
Publisher: Taylor & Francis Ltd

Numerical simulation models play a crucial role in various engineering disciplines by enabling the modeling and understanding of physical phenomena. These models serve as powerful tools for identifying problems, optimizing behavior, and gaining insights into the underlying mechanisms of the phenomena under study. One innovative approach to numerical simulation is Smart Proxy Modeling, which leverages advanced artificial intelligence (AI) and machine learning techniques to replicate numerical simulations with exceptional accuracy.

Smart Proxy Models offer several advantages over traditional numerical simulations. Firstly, they can be executed on a laptop in a matter of minutes, significantly reducing the time and computational resources required for complex simulations. This makes it easier and more accessible for engineers, researchers, and practitioners to utilize numerical simulations, even in resource-constrained environments.

Secondly, Smart Proxy Models can replicate numerical simulations with high precision, often achieving accuracies comparable to those achieved through traditional numerical methods. This level of accuracy is crucial in fields such as reservoir simulation, where small variations in input parameters can have significant impacts on the results. By leveraging AI and machine learning algorithms, Smart Proxy Models can learn from historical data and make predictions with remarkable accuracy, enabling engineers to make informed decisions based on simulated outcomes.

Thirdly, Smart Proxy Models simplify the use of complex numerical simulations by automating the process of model development. Traditional numerical simulations require extensive expertise and knowledge of numerical methods, which can be challenging for beginners and non-experts. With Smart Proxy Models, the development process is streamlined, allowing users to create accurate models with minimal technical expertise.

In this book, we delve into the realm of Smart Proxy Modeling, providing readers with a comprehensive guide on how to develop Smart Proxy Models using AI and machine learning. We cover various topics, including the principles of Smart Proxy Modeling, the techniques used for model training and validation, and the application of Smart Proxy Models in real-world cases.

We begin by introducing the concept of Smart Proxy Modeling and its significance in numerical simulation. We then discuss the principles of AI and machine learning, which are essential for developing Smart Proxy Models. We explore different training algorithms, such as deep learning and genetic programming, and their applications in replicating numerical simulations.

Furthermore, we showcase real-world case studies based on commercially available simulators, demonstrating the practical applications of Smart Proxy Modeling in various engineering disciplines. These case studies include reservoir simulation, computational fluid dynamics, and mechanical engineering, highlighting the benefits and limitations of Smart Proxy Models in each application.

Smart Proxy Modeling is particularly valuable for petroleum, chemical, environmental, and mechanical engineers, as well as statisticians and others working with applications of data-driven analytics. By leveraging Smart Proxy Models, engineers can optimize their designs, reduce costs, and improve efficiency in their operations. Statisticians can use Smart Proxy Models to analyze large datasets and make predictions with high accuracy, enabling them to make informed decisions in fields such as marketing, finance, and healthcare.

In conclusion, Smart Proxy Modeling is a transformative approach to numerical simulation that leverages advanced AI and machine learning techniques to replicate numerical simulations with exceptional accuracy. This book provides readers with a comprehensive guide on how to develop Smart Proxy Models, covering topics such as model training, validation, and application in real-world cases. With Smart Proxy Modeling, engineers, researchers, and practitioners can gain deeper insights into physical phenomena, optimize behavior, and make informed decisions based on data-driven analytics.

Weight: 540g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032151144