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April 24, 2026Scientific ReportsOpen Access

Physics-informed structural diagnostics of model–data agreement beyond scalar metrics

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Authors

HHHedayat HaddadiAKAdam KloskowskiPMPiotr Mironowicz

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Overview

Framework diagnoses model-data agreement in thermodynamic systems, highlighting structural biases and artefacts.

Key Points

  • The aim is to introduce a new framework for validating physics-informed machine learning models by assessing model-data agreement.
  • Developed the Agreement–Entropy Map (AEM) framework based on a physical motivation and regression geometry.
  • Evaluated AEM against matched comparison domains without requiring distributional assumptions.
  • Applies uniformly across experiment-experiment and model-experiment comparisons.
  • AEM reveals structural bias and artefacts that traditional scalar metrics miss.
  • Demonstrates the ability to identify when stochastic interpretations are valid within a shared physical framework.
  • Provides a more interpretable validation approach in scenarios with limited or varied data.

Cite This Study

Haddadi et al. (2026) studied this question.

synapsesocial.com/papers/69eb0ac4553a5433e34b4b48https://doi.org/10.1038/s41598-026-49445-8
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