In this work, Bayesian inversion with global-local forwards models is used to identify the parameters based on hydraulic fractures in porous media. It is well-known that using Bayesian inversion to identify material parameters is computationally expensive. Although each sampling may take more than one hour, thousands of samples are required to capture the target density. Thus, instead of using fine-scale high-fidelity simulations, we use a non-intrusive global-local (GL) approach for the forward model. We further extend prior work to a large deformation setting based on the Neo-Hookean strain energy function. The resulting framework is described in detail and substantiated with some numerical tests.

翻译：本文采用基于全局-局部前向模型的贝叶斯反演方法，识别多孔介质中水力裂缝的参数。众所周知，利用贝叶斯反演识别材料参数的计算成本极高。尽管每次采样可能需要超过一小时，但为捕获目标密度仍需数千次采样。因此，我们采用非侵入式全局-局部（GL）方法作为前向模型，而非细尺度高保真模拟。此外，我们将先前工作扩展至基于Neo-Hookean应变能函数的大变形场景。文中详细描述了该框架，并通过数值试验予以验证。