Physics-Informed Neural Networks (PINNs) have emerged as a highly active research topic across multiple disciplines in science and engineering, including computational geomechanics. PINNs offer a promising approach in different applications where faster, near real-time or real-time numerical prediction is required. Examples of such areas in geomechanics include geotechnical design optimization, digital twins of geo-structures and stability prediction of monitored slopes. But there remain challenges in training of PINNs, especially for problems with high spatial and temporal complexity. In this paper, we study how the training of PINNs can be improved by using an idealized poroelasticity problem as a demonstration example. A curriculum training strategy is employed where the PINN model is trained gradually by dividing the training data into intervals along the temporal dimension. We find that the PINN model with curriculum training takes nearly half the time required for training compared to conventional training over the whole solution domain. For the particular example here, the quality of the predicted solution was found to be good in both training approaches, but it is anticipated that the curriculum training approach has the potential to offer a better prediction capability for more complex problems, a subject for further research.

翻译：物理信息神经网络（PINNs）已成为科学与工程多学科领域（包括计算地质力学）高度活跃的研究课题。在需要快速、近实时或实时数值预测的不同应用场景中，PINNs提供了一种有前景的方法。地质力学中的此类应用实例包括岩土工程设计优化、地质结构数字孪生以及监测边坡的稳定性预测。然而，PINNs的训练仍面临挑战，尤其是在处理具有高时空复杂性的问题时。本文以理想化孔隙弹性问题作为演示案例，研究如何改进PINNs的训练。通过沿时间维度将训练数据划分为区间，采用课程训练策略对PINN模型进行逐步训练。研究发现，采用课程训练的PINN模型所需训练时间仅为传统全域训练方法的一半。针对本文具体示例，两种训练方法预测解的质量均表现良好，但预期课程训练方法在更复杂问题中具有提供更优预测能力的潜力，这有待进一步研究。