The multiple-network poroelasticity (MPET) equations describe deformation and pressures in an elastic medium permeated by interacting fluid networks. In this paper, we (i) place these equations in the theoretical context of coupled elliptic-parabolic problems, (ii) use this context to derive residual-based a-posteriori error estimates and indicators for fully discrete MPET solutions and (iii) evaluate the performance of these error estimators in adaptive algorithms for a set of test cases: ranging from synthetic scenarios to physiologically realistic simulations of brain mechanics.

翻译：在本文中,我们(一) 将这些方程置于与椭圆paroplic-parepolic问题有关的理论背景中,(二) 利用这一背景得出基于残余的外在误差估计值和完全离散的MPET解决方案指标,(三) 评估这些误差测算器在一系列测试案例的适应性算法中的性能:从合成情景到脑力学生理上现实的模拟。