A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications

A methodology to assess and improve the physics consistency of an artificial neural network regression model for engineering applications

Abstract In recent times, artificial neural networks (ANNs) have become the popular choice of model for researchers while performing regression analysis between inputs and output. However; in scientific and engineering applications, developed ANN regression model is often found to be inconsistent wi...

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Bibliographic Details
Main Author: E. Rajasekhar Nicodemus
Format: Article
Language:English
Published: SpringerOpen 2022-07-01
Series:Advanced Modeling and Simulation in Engineering Sciences
Subjects:
Neural network regression model
Taylor series
Radius of convergence
Degree of Taylor polynomial
Physics consistency
Physics loss function
Online Access:https://doi.org/10.1186/s40323-022-00224-w

Internet

https://doi.org/10.1186/s40323-022-00224-w

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