  Due to the computational complexity of micro-swimmer models with fully
resolved hydrodynamics, parameter estimation has been prohibitively expensive.
Here, we describe a Bayesian uncertainty quantification framework that is
highly parallelizable, making parameter estimation for complex forward models
tractable. Using noisy in-silico data for swimmers, we demonstrate the
methodology's robustness in estimating the fluid and elastic swimmer
parameters. Our proposed methodology allows for analysis of real data and
demonstrates potential for parameter estimation for various types of
micro-swimmers. Better understanding the movement of elastic micro-structures
in a viscous fluid could aid in developing artificial micro-swimmers for
bio-medical applications as well as gain a fundamental understanding of the
range of parameters that allow for certain motility patterns.

Quantitative Methods;Numerical Analysis;Numerical Analysis;Biological Physics;Fluid Dynamics