Mechanical modelling of poroelastic media under finite strain is usually carried out via phenomenological models neglecting complex micro-macro scales interdependency. One reason is that the mathematical two-scale analysis is only straightforward assuming infinitesimal strain theory. Exploiting the potential of ANNs for fast and reliable upscaling and localisation procedures, we propose an incremental numerical approach that considers rearrangement of the cell properties based on its current deformation, which leads to the remodelling of the macroscopic model after each time increment. This computational framework is valid for finite strain and large deformation problems while it ensures infinitesimal strain increments within time steps. The full effects of the interdependency between the properties and response of macro and micro scales are considered for the first time providing more accurate predictive analysis of fluid-saturated porous media which is studied via a numerical consolidation example. Furthermore, the (nonlinear) deviation from Darcy’s law is captured in fluid filtration numerical analyses. Finally, the brain tissue mechanical response under uniaxial cyclic test is simulated and studied.

多孔弹性介质在有限应变下的力学建模通常是通过忽略复杂的宏观尺度相互依赖性的现象学模型进行的。原因之一是数学二阶分析仅在假设无穷小应变理论的情况下才简单明了。利用人工神经网络在快速可靠的升级和定位过程中的潜力，我们提出了一种增量数值方法，该方法考虑了基于其当前变形的单元格属性的重排，从而导致每次时间增量后宏观模型的重塑。此计算框架适用于有限应变和大变形问题，同时可确保在时间步长内产生无限小的应变增量。首次考虑了宏观和微观尺度的性质与响应之间的相互依赖性的全部影响，从而提供了对流体饱和多孔介质的更准确的预测分析，并通过数值固结实例进行了研究。此外，在流体过滤数值分析中捕获了与达西定律的（非线性）偏差。最后，对单轴循环试验下的脑组织机械反应进行了模拟和研究。