Physics-informed deep learning for one-dimensional consolidation

Abstract

Neural networks with physical governing equations as constraints have recently created a new trend in machine learning research. In this context, a review of related research is first presented and discussed. The potential offered by such physics-informed deep learning models for computations in geomechanics is demonstrated by application to one-dimensional (1D) consolidation. The governing equation for 1D problems is applied as a constraint in the deep learning model. The deep learning model relies on automatic differentiation for applying the governing equation as a constraint, based on the mathematical approximations established by the neural network. The total loss is measured as a combination of the training loss (based on analytical and model predicted solutions) and the constraint loss (a requirement to satisfy the governing equation). Two classes of problems are considered: forward and inverse problems. The forward problems demonstrate the performance of a physically constrained neural network model in predicting solutions for 1D consolidation problems. Inverse problems show prediction of the coefficient of consolidation. Terzaghi’s problem, with varying boundary conditions, is used as a numerical example and the deep learning model shows a remarkable performance in both the forward and inverse problems. While the application demonstrated here is a simple 1D consolidation problem, such a deep learning model integrated with a physical law has significant implications for use in, such as, faster real-time numerical prediction for digital twins, numerical model reproducibility and constitutive model parameter optimization.