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DOI: 10.14569/IJACSA.2025.0161294
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Functions Inverse Using Neural Networks via Branch-Wise Decomposition and Newton Refinement

Author 1: Abdullah Balamash

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 16 Issue 12, 2025.

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Abstract: In this work, a unified framework (using Neural Networks) is proposed to find the inverse of mathematical functions, spanning both simple one-to-one mapping and complex multivalued relations. The approach uses standard multilayer Neural Networks (NN) to approximate the functions’ inverse and introduces a deterministic branch-wise decomposition to handle multi-valued inverses. For single-valued (one-to-one) functions, a NN is directly trained on input-output pairs to learn the inverse mapping. For multi-valued functions, the function domain is decomposed into one-to-one branches, and a dedicated NN is trained for each branch. A refinement step using Newton’s method is applied to the NN output to further improve inversion accuracy. Across a broad set of benchmark functions, the proposed approach achieved low mean absolute error (MAE) and mean squared error (MSE) in recovering the true inverse, with high round-trip consistency. Newton refinement further reduces inversion error by rapidly converging to higher precision solutions. Notably, even for multi-valued inverse functions, each branch-specific NN can accurately recover the true inverse. Accordingly, standard NN, when combined with branch-wise decomposition and Newton refinement, can serve as an effective universal approximator for the inverse of functions across a spectrum of complexities.

Keywords: Neural networks; function inverse; Newton method; branch-wise decomposition

Abdullah Balamash. “Functions Inverse Using Neural Networks via Branch-Wise Decomposition and Newton Refinement”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161294

@article{Balamash2025,
title = {Functions Inverse Using Neural Networks via Branch-Wise Decomposition and Newton Refinement},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161294},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161294},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Abdullah Balamash}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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