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 smoothness. We propose a framework that 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 the damage field of an airfoil under transonic buffeting.

翻译：在目前的工作中,我们注重大规模流体结构互动系统,其特点是结构结构变形。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,它建立在不突出的卡尔曼过滤器/转换到高效校准上,并用吵闹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,对于计算成本昂贵并作为黑盒提供的远期模型具有特别价值,因为黑盒是无法区分的。通过成功校准活塞问题的模型参数和确定透音式餐下空气油的损害领域,该框架得到验证和验证。