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Prophecies using Physics Involved Neural Networks (PINNs) for achieving the accuracy using AI Models in discrete Kinematics

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  • Prophecies using Physics Involved Neural Networks (PINNs) for achieving the accuracy using AI Models in discrete Kinematics

Ashok Kumar Ramadoss 1, 2, 3, *

1 Coimbatore Institute of Technology, Anna University.

2 Member in Society of Robotics Surgery, Orlando Florida, Senior Member in UACEE New York.

3 Senior Member in Hong Kong society of robotics and automation.

Research Article

International Journal of Science and Research Archive, 2025, 16(01), 444-453

Article DOI: 10.30574/ijsra.2025.16.1.2043

DOI url: https://doi.org/10.30574/ijsra.2025.16.1.2043

Received on 27 May 2025; revised on 05 July 2025; accepted on 07 July 2025

Artificial Nonmonotonic Neural schema or Networks (ANNNs), a kind of hybrid learning systems that are capable of nonmonotonic reasoning. Nonmonotonic reasoning plays an important role in the development of artificial intelligent systems that try to mimic common sense reasoning, as exhibited by humans in slow and steady but the error is minimized unlike in monotonic where the decision is fast but with more errors. on the other hand, a hybrid learning system provides an explanation capability to trained Neural Networks through acquiring symbolic knowledge of a domain, refining it using a set of classified examples along with Connectionist learning techniques and, finally, extracting comprehensible symbolic information.

PINNs; ANNNs; BDA; KNN; SVM

https://journalijsra.com/sites/default/files/fulltext_pdf/IJSRA-2025-2043.pdf

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Ashok Kumar Ramadoss. Prophecies using Physics Involved Neural Networks (PINNs) for achieving the accuracy using AI Models in discrete Kinematics. International Journal of Science and Research Archive, 2025, 16(01), 444-453. Article DOI: https://doi.org/10.30574/ijsra.2025.16.1.2043.

Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0

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