A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling

Oleg Agamalov

doi:doi:10.11648/j.ijiis.20251403.12

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A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling

Oleg Agamalov^*

Published in International Journal of Intelligent Information Systems (Volume 14, Issue 3)

Received: 16 June 2025 Accepted: 27 June 2025 Published: 28 July 2025

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Abstract

This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability, robust uncertainty handling, and adaptability to nonlinear relationships, applying the expert knowledge in the considered area, while PINN excels at incorporating physical laws, enhancing data efficiency, and improving generalization. The synergistic combination is envisioned to yield a more robust, interpretable, and physically consistent artificial intelligence (AI) solution, particularly for complex scientific and engineering problems characterized by nonlinearity, uncertainty, and sparse data, based on measurement data, a nonformal human expert's experience, and formal known physical laws. This paper details the foundational principles of ANFIS and PINN, outlines the compelling rationale for their integration, proposes several conceptual architectures and implementation strategies, and discusses the challenges and future directions for this promising hybrid AI paradigm.

Published in	International Journal of Intelligent Information Systems (Volume 14, Issue 3)
DOI	10.11648/j.ijiis.20251403.12
Page(s)	60-69
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Adaptive Neuro-fuzzy Inference Systems (ANFIS), Artificial Intelligence (AI), Complex System, Physics-informed Neural Networks (PINN)

References

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APA Style

Agamalov, O. (2025). A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling. International Journal of Intelligent Information Systems, 14(3), 60-69. https://doi.org/10.11648/j.ijiis.20251403.12

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ACS Style

Agamalov, O. A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling. Int. J. Intell. Inf. Syst. 2025, 14(3), 60-69. doi: 10.11648/j.ijiis.20251403.12

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AMA Style

Agamalov O. A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling. Int J Intell Inf Syst. 2025;14(3):60-69. doi: 10.11648/j.ijiis.20251403.12

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@article{10.11648/j.ijiis.20251403.12,
  author = {Oleg Agamalov},
  title = {A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {14},
  number = {3},
  pages = {60-69},
  doi = {10.11648/j.ijiis.20251403.12},
  url = {https://doi.org/10.11648/j.ijiis.20251403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20251403.12},
  abstract = {This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability, robust uncertainty handling, and adaptability to nonlinear relationships, applying the expert knowledge in the considered area, while PINN excels at incorporating physical laws, enhancing data efficiency, and improving generalization. The synergistic combination is envisioned to yield a more robust, interpretable, and physically consistent artificial intelligence (AI) solution, particularly for complex scientific and engineering problems characterized by nonlinearity, uncertainty, and sparse data, based on measurement data, a nonformal human expert's experience, and formal known physical laws. This paper details the foundational principles of ANFIS and PINN, outlines the compelling rationale for their integration, proposes several conceptual architectures and implementation strategies, and discusses the challenges and future directions for this promising hybrid AI paradigm.},
 year = {2025}
}

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TY  - JOUR
T1  - A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling

AU  - Oleg Agamalov
Y1  - 2025/07/28
PY  - 2025
N1  - https://doi.org/10.11648/j.ijiis.20251403.12
DO  - 10.11648/j.ijiis.20251403.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
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PB  - Science Publishing Group
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AB  - This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability, robust uncertainty handling, and adaptability to nonlinear relationships, applying the expert knowledge in the considered area, while PINN excels at incorporating physical laws, enhancing data efficiency, and improving generalization. The synergistic combination is envisioned to yield a more robust, interpretable, and physically consistent artificial intelligence (AI) solution, particularly for complex scientific and engineering problems characterized by nonlinearity, uncertainty, and sparse data, based on measurement data, a nonformal human expert's experience, and formal known physical laws. This paper details the foundational principles of ANFIS and PINN, outlines the compelling rationale for their integration, proposes several conceptual architectures and implementation strategies, and discusses the challenges and future directions for this promising hybrid AI paradigm.
VL  - 14
IS  - 3
ER  -

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Author Information

Oleg Agamalov

Independent Researcher, Pivdennoukrainsk, Ukraine

Contact Email

http://orcid.org/0000-0002-0330-8268

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APA Style

Agamalov, O. (2025). A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling. International Journal of Intelligent Information Systems, 14(3), 60-69. https://doi.org/10.11648/j.ijiis.20251403.12

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ACS Style

Agamalov, O. A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling. Int. J. Intell. Inf. Syst. 2025, 14(3), 60-69. doi: 10.11648/j.ijiis.20251403.12

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AMA Style

Agamalov O. A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling. Int J Intell Inf Syst. 2025;14(3):60-69. doi: 10.11648/j.ijiis.20251403.12

Copy | Download

@article{10.11648/j.ijiis.20251403.12,
  author = {Oleg Agamalov},
  title = {A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {14},
  number = {3},
  pages = {60-69},
  doi = {10.11648/j.ijiis.20251403.12},
  url = {https://doi.org/10.11648/j.ijiis.20251403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20251403.12},
  abstract = {This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability, robust uncertainty handling, and adaptability to nonlinear relationships, applying the expert knowledge in the considered area, while PINN excels at incorporating physical laws, enhancing data efficiency, and improving generalization. The synergistic combination is envisioned to yield a more robust, interpretable, and physically consistent artificial intelligence (AI) solution, particularly for complex scientific and engineering problems characterized by nonlinearity, uncertainty, and sparse data, based on measurement data, a nonformal human expert's experience, and formal known physical laws. This paper details the foundational principles of ANFIS and PINN, outlines the compelling rationale for their integration, proposes several conceptual architectures and implementation strategies, and discusses the challenges and future directions for this promising hybrid AI paradigm.},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - A Hybrid Adaptive Neuro-fuzzy Inference System and Physics-informed Neural Network (ANFIS-PINN) for Complex System Modeling

AU  - Oleg Agamalov
Y1  - 2025/07/28
PY  - 2025
N1  - https://doi.org/10.11648/j.ijiis.20251403.12
DO  - 10.11648/j.ijiis.20251403.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 60
EP  - 69
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20251403.12
AB  - This work explores the integration of Adaptive Neuro-Fuzzy Inference Systems (ANFIS) and Physics-Informed Neural Networks (PINN) into a novel hybrid ANFIS-PINN framework. The proposed system aims to leverage the complementary strengths of both paradigms to address limitations inherent in individual approaches. ANFIS offers inherent interpretability, robust uncertainty handling, and adaptability to nonlinear relationships, applying the expert knowledge in the considered area, while PINN excels at incorporating physical laws, enhancing data efficiency, and improving generalization. The synergistic combination is envisioned to yield a more robust, interpretable, and physically consistent artificial intelligence (AI) solution, particularly for complex scientific and engineering problems characterized by nonlinearity, uncertainty, and sparse data, based on measurement data, a nonformal human expert's experience, and formal known physical laws. This paper details the foundational principles of ANFIS and PINN, outlines the compelling rationale for their integration, proposes several conceptual architectures and implementation strategies, and discusses the challenges and future directions for this promising hybrid AI paradigm.
VL  - 14
IS  - 3
ER  -

Copy | Download