To advance our understanding of the movement of elastic microstructures in a viscous fluid, techniques that utilize available data to estimate model parameters are necessary. Here, we describe a Bayesian uncertainty quantification framework that is highly parallelizable, making parameter estimation tractable for complex fluid-structure interaction models. Using noisy in silico data for swimmers, we demonstrate the methodology's robustness in estimating fluid and elastic swimmer parameters, along with their uncertainties. We identify correlations between model parameters and gain insight into emergent swimming trajectories of a single swimmer or a pair of swimmers. Our proposed framework can handle data with a spatiotemporal resolution representative of experiments, showing that this framework can be used to aid in the development of artificial micro-swimmers for biomedical applications, as well as gain a fundamental understanding of the range of parameters that allow for certain motility patterns.

中文翻译：

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用于估计微型游泳者模型中流体和材料参数的贝叶斯框架

为了加深我们对粘性流体中弹性微结构运动的理解，需要利用可用数据来估计模型参数的技术。在这里，我们描述了一个高度并行化的贝叶斯不确定性量化框架，使参数估计易于处理复杂的流固耦合模型。使用游泳者的噪声计算机数据，我们证明了该方法在估计流体和弹性游泳者参数及其不确定性方面的稳健性。我们确定模型参数之间的相关性，并深入了解单个游泳者或一对游泳者的紧急游泳轨迹。我们提出的框架可以处理具有代表实验的时空分辨率的数据，