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Updated: Jul 10, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Automatic machine learning versus human knowledge-based models, property-based models and the fatigue problem.

Enrique Castillo1, Alfonso Fernández Canteli2, Miguel Muñiz Calvente2

  • 1University of Cantabria, Santander, Cantabria, Spain.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|November 19, 2023
PubMed
Summary
This summary is machine-generated.

Human knowledge is crucial for developing reliable property-based models, especially in fatigue analysis. Data-driven methods alone are insufficient, necessitating hybrid approaches combining human expertise with data for robust predictions.

Keywords:
AI based on propertiesS–N fieldcombined knowledgecompatibilityhuman knowledgenormalizing variable

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Area of Science:

  • Engineering
  • Materials Science
  • Artificial Intelligence

Background:

  • Data-driven methods, like machine learning, face limitations in fatigue analysis due to insufficient data and lack of interpretability.
  • Black-box models fail to provide a fundamental understanding of fatigue phenomena and hinder extrapolation beyond experimental data.
  • Existing fatigue models (S-N, GRV-N) require extensive data and struggle to incorporate crucial parameters like stress ratio (R).

Purpose of the Study:

  • To emphasize the importance of human-based knowledge in scientific modeling.
  • To introduce original property-based models that guarantee non-arbitrary parametric solutions.
  • To present a hybrid approach combining human expertise with data-driven parameter estimation for complex problems.

Main Methods:

  • Development of property-based models formulated with equations ensuring satisfaction and non-arbitrary parameters.
  • Integration of human-based knowledge as the core component of artificial intelligence mixed models.
  • Application of data-driven techniques for parameter estimation within the human-guided modeling framework.

Main Results:

  • Demonstration that property-based models overcome limitations of purely data-driven approaches in fatigue analysis.
  • Successful application of the proposed methodology to fatigue S-N and GRV-N models, ensuring comprehension and extrapolation capabilities.
  • Validation of the hybrid approach's generalizability to other scientific and engineering problems beyond fatigue.

Conclusions:

  • Human-based knowledge is essential for creating robust, interpretable, and generalizable scientific models.
  • Artificial intelligence mixed models offer a superior alternative to purely data-driven methods for complex phenomena like material fatigue.
  • The presented methodology provides a framework for extending property-based modeling to diverse scientific challenges, enhancing predictive accuracy and understanding.