论文标题
使用物理信息的神经网络对非等热多相力学的反向建模
Inverse modeling of nonisothermal multiphase poromechanics using physics-informed neural networks
论文作者
论文摘要
我们提出了一种在多孔培养基中使用物理知识的神经网络（PINN）在多孔培养基中的多相热力学（THM）过程中的参数识别的解决方案策略。我们采用无量纲的理事方程式，特别适合逆问题，我们利用了我们先前工作中开发的顺序多物理Pinn求解器。我们在多个基准问题上验证了所提出的反模型方法，包括Terzaghi的等温固结问题，Barry-Mercer的等温注射产生问题以及非饱和土壤层的非等热整合。我们报告了提出的顺序PINN-THM反求器的出色性能，从而为将PINNS应用于复杂非线性多物理问题的逆建模铺平了道路。
We propose a solution strategy for parameter identification in multiphase thermo-hydro-mechanical (THM) processes in porous media using physics-informed neural networks (PINNs). We employ a dimensionless form of the THM governing equations that is particularly well suited for the inverse problem, and we leverage the sequential multiphysics PINN solver we developed in previous work. We validate the proposed inverse-modeling approach on multiple benchmark problems, including Terzaghi's isothermal consolidation problem, Barry-Mercer's isothermal injection-production problem, and nonisothermal consolidation of an unsaturated soil layer. We report the excellent performance of the proposed sequential PINN-THM inverse solver, thus paving the way for the application of PINNs to inverse modeling of complex nonlinear multiphysics problems.
