Physics-Informed Neural Network Solution of Thermo-Hydro-Mechanical Processes in Porous Media
Physics-Informed Neural Network Solution of Thermo-Hydro-Mechanical Processes in Porous Media
复制标题
DOI：
10.1061/(asce)em.1943-7889.0002156
复制
发表时间：
2022-11-01
影响因子：
3.3
通讯作者：
Juanes, Rubn
中科院分区：
文献类型：
--
作者：
Amini, Danial;Haghighat, Ehsan;Juanes, Rubn
物理信息神经网络（PINN）在偏微分方程（PDE）问题的正、逆和代理建模方面受到越来越多的关注。然而，他们的应用程序多物理场问题，由几个耦合偏微分方程，提出了独特的挑战，阻碍了这种方法的鲁棒性和广泛的适用性。在这里，我们研究的应用PINNs的前向解决方案的问题，涉及热-水-机械（THM）过程中的多孔介质，表现出不同的空间和时间尺度的热导率，透水性，和弹性。此外，PINNs面临着多目标和非凸性质的优化问题的挑战。为了解决这些基本问题，我们（1）以最适合深度学习算法的无量纲形式重写了THM控制方程，（2）提出了一种顺序训练策略，该策略避免了对多物理场问题的同时解决方案的需求，并促进了优化器在解决方案搜索中的任务，（3）利用自适应权重策略克服多目标优化问题梯度流中的刚性。最后，我们应用这个框架的解决方案，在一个和两个维度的几个综合问题。(C)2022年美国土木工程师学会
Physics-informed neural networks (PINNs) have received increased interest for forward, inverse, and surrogate modeling of problems described by partial differential equations (PDEs). However, their application to multiphysics problem, governed by several coupled PDEs, presents unique challenges that have hindered the robustness and widespread applicability of this approach. Here we investigate the application of PINNs to the forward solution of problems involving thermo-hydro-mechanical (THM) processes in porous media that exhibit disparate spatial and temporal scales in thermal conductivity, hydraulic permeability, and elasticity. In addition, PINNs are faced with the challenges of the multiobjective and nonconvex nature of the optimization problem. To address these fundamental issues, we (1) rewrote the THM governing equations in dimensionless form that is best suited for deep learning algorithms, (2) propose a sequential training strategy that circumvents the need for a simultaneous solution of the multiphysics problem and facilitates the task of optimizers in the solution search, and (3) leveraged adaptive weight strategies to overcome the stiffness in the gradient flow of the multiobjective optimization problem. Finally, we applied this framework to the solution of several synthetic problems in one and two dimensions. (C) 2022 American Society of Civil Engineers.