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Systematic structural discrepancy assessment for computer models.

Michael Goldstein1, Ian Vernon1, Jonathan A Cumming1

  • 1Department of Mathematical Sciences, Durham University, Durham, UK.

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|April 2, 2025
PubMed
Summary
This summary is machine-generated.

This study defines and distinguishes internal and external model discrepancy in computer simulators. It introduces a practical method for assessing structural discrepancy using simulator experiments, demonstrated with a biological model.

Keywords:
computer modelsdiscrepancyemulationmodel inadequacy

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Area of Science:

  • Computational modeling and simulation
  • Uncertainty quantification in biological systems

Background:

  • Model or structural discrepancy quantifies differences between computer simulator outputs and real-world systems.
  • Discrepancy arises from model simplifications, implementation choices, and unknown or undiscovered scientific features.
  • Distinguishing between internal and external discrepancy is crucial for accurate simulator analysis.

Purpose of the Study:

  • To define and differentiate internal and external model discrepancy.
  • To present a tractable methodology for assessing structural discrepancy in computer models.
  • To illustrate the proposed methodology using a biological model.

Main Methods:

  • Defining internal discrepancy as assessable via simulator experiments.
  • Defining external discrepancy as outside the scope of internal experiments.
  • Developing a workflow for structural discrepancy assessment using experimental data from the computer model.

Main Results:

  • Successfully defined and distinguished internal and external discrepancy.
  • Presented a practical methodology for assessing structural discrepancy.
  • Demonstrated the methodology's application on a simple biological model.

Conclusions:

  • The proposed methodology offers a tractable approach to assessing structural discrepancy.
  • Understanding and quantifying discrepancy is vital for reliable computer simulations in biology and healthcare.
  • This work contributes to uncertainty quantification in complex systems.