Bayesian calibration is widely used for inverse analysis and uncertainty analysis for complex systems in the presence of both computer models and observation data. In the present work, we focus on large-scale fluid-structure interaction systems characterized by large structural deformations. Numerical methods to solve these problems, including embedded/immersed boundary methods, are typically not differentiable and lack of smoothness. We propose a framework which is built on unscented Kalman filter/inversion to efficiently calibrate and provide uncertainty estimations of such complicated models with noisy observation data. The approach is derivative-free and non-intrusive, and is of particular value for the forward model that is computationally expensive and provided as a black box which is impractical to differentiate. The framework is demonstrated and validated by successfully calibrating the model parameters of a piston problem and identifying damage field of an airfoil under transonic buffeting.

翻译：在目前的工作中,我们侧重于具有大规模结构变形特征的大型流体结构互动系统; 解决这些问题的数值方法,包括嵌入/浸入边界方法,通常无法区分,而且缺乏光滑性; 我们提出了一个框架,它建立在不突出的Kalman过滤/转换到高效校准的基础上,用噪音的观测数据对这种复杂模型提供不确定性的估计; 这种方法是无衍生物和非侵入性的,对于计算成本昂贵的远期模型具有特别价值,并且作为黑盒提供,无法区分; 成功地校准活塞问题模型参数,确定透音式餐中气流的损害领域,以此来证明和验证该框架。