Semi Empirical Neural Network Modeling and Digital Twins Development

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Download or read online Semi Empirical Neural Network Modeling and Digital Twins Development PDF book by Dmitriy Tarkhov in ePUB, PDF or Kindle eBooks. Published by Academic Press in 01 November 2019 the book become immediate popular and critical acclaim in books with total hardcover page 288. Click Download Book button to get book file. Read some info about this book below.

  • Author : Dmitriy Tarkhov
  • Publisher : Academic Press
  • Release : 01 November 2019
  • ISBN : 0128156511
  • Page : 288 pages
  • Language : English
  • Total View : 7561 Views
  • File Size : 53,8 Mb

Semi Empirical Neural Network Modeling and Digital Twins Development Book PDF summary

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. Offers a new approach to neural networks using a unified simulation model at all stages of design and operation Illustrates this new approach with numerous concrete examples throughout the book Presents the methodology in separate and clearly-defined stages

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Semi-empirical Neural Network Modeling and Digital Twins Development PDF book is popular book written by Dmitriy Tarkhov,Alexander Nikolayevich Vasilyev. The book was released by Academic Press in 23 November 2019 with total hardcover pages 288. 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

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