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This study introduces a new framework combining artificial intelligence (AI) and uncertainty quantification for predictive modeling with small, noisy datasets. It enables trustworthy, explainable AI models by integrating expert knowledge and physics-based approaches.

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Area of Science:

  • Multidisciplinary research at the intersection of data science, physics, chemistry, and engineering.
  • Development of advanced computational and mathematical frameworks for scientific modeling.

Background:

  • Traditional data science and machine learning methods are inadequate for small, correlated, and noisy datasets common in physics, chemistry, and engineering.
  • Existing approaches lack systematic frameworks for integrating expert knowledge into physics-based models under uncertainty.

Purpose of the Study:

  • To develop a mathematical and computational framework for probabilistic artificial intelligence (AI)-based predictive modeling.
  • To combine data, expert knowledge, multiscale models, and information theory using uncertainty quantification and probabilistic graphical models (PGMs).
  • To provide predictive guarantees for PGMs and demonstrate their application in chemistry.

Main Methods:

  • Development of a novel framework integrating AI, uncertainty quantification, and probabilistic graphical models (PGMs).
  • Incorporation of expert knowledge, multiscale models, and information theory.
  • Application of the framework to a microkinetic model of the oxygen reduction reaction.

Main Results:

  • A demonstrated framework for probabilistic AI-based predictive modeling applicable to small, correlated datasets.
  • Development of predictive guarantees for probabilistic graphical models (PGMs).
  • Successful application to a specific chemical reaction (oxygen reduction reaction).

Conclusions:

  • The proposed framework offers explainable results, leading to correctable and trustworthy AI models.
  • This approach effectively combines diverse information sources (data, expert knowledge, physics-based models) for enhanced predictive power.
  • The framework addresses critical limitations of current methods in scientific data analysis and modeling.