Semi-empirical Neural Network Modeling and Digital Twins Development
Semi-empirical Neural Network Modeling and Digital Twins Development by Dmitriy Tarkhov, published by Elsevier Science on November 22, 2019, is a comprehensive exploration of a novel approach to constructing multilayered neural network solutions for differential equations. This edition spans 288 pages and is presented in English. The book addresses the limitations of current neural network methods, particularly their lengthy learning processes and reliance on single-layered networks based on the finite element method.
Readers will find that this book introduces a method that automatically incorporates task parameters into the final solution formula, streamlining the modeling process. It emphasizes the construction of individual models with unique features, making it relevant for those in the fields of science, biotechnology, and technology and engineering. Through a variety of specific problems, both hypothetical and practical, the text illustrates key concepts, providing a solid foundation for understanding advanced neural network modeling techniques.
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Semi-empirical Neural Network Modeling presents a new approach on how to quickly construct an accurate, multilayered neural network solution of differential equations. Current neural network methods have significant disadvantages, including a lengthy learning process and single-layered neural networks built on the finite element method (FEM). The strength of the new method presented in this book is the automatic inclusion of task parameters in the final solution formula, which eliminates the need for repeated problem-solving. This is especially important for constructing individual models with unique features. The book illustrates key concepts through a large number of specific problems, both hypothetical models and practical interest.
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