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A Bayesian Framework to Estimate Fluid and Material Parameters in Micro-swimmer Models.

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This study introduces a Bayesian framework to estimate parameters for elastic microswimmers in fluid. The method accurately quantizes uncertainties, aiding in the design of artificial micro-swimmers.

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Area of Science:

  • Fluid dynamics
  • Biophysics
  • Computational modeling

Background:

  • Understanding microswimmer movement in viscous fluids requires accurate parameter estimation.
  • Complex fluid-structure interaction models necessitate efficient parameter estimation techniques.

Purpose of the Study:

  • To develop a robust and parallelizable Bayesian uncertainty quantification framework for parameter estimation in microswimmer models.
  • To demonstrate the framework's ability to estimate fluid and elastic parameters, including uncertainties, using simulated data.
  • To gain insights into microswimmer behavior and motility patterns.

Main Methods:

  • A highly parallelizable Bayesian uncertainty quantification framework was developed.
  • The framework was applied to in silico data from elastic microswimmers.
  • Parameter estimation and uncertainty quantification were performed for fluid and elastic properties.

Main Results:

  • The methodology accurately estimated microswimmer parameters and their uncertainties from noisy data.
  • Correlations between model parameters were identified, providing deeper insights.
  • The framework demonstrated robustness with data resolution comparable to experimental conditions.

Conclusions:

  • The proposed Bayesian framework is effective for parameter estimation in complex fluid-structure interaction models.
  • This approach can significantly aid in the development of artificial micro-swimmers for biomedical applications.
  • The framework facilitates a fundamental understanding of parameters governing microswimmer motility.