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 methodologys 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.
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