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 ideal-ized poroelasticity problem as a demonstration example. A curriculum training strat-egy 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 con-ventional 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 approach-es, 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个学科（包括计算岩土力学）中的高度活跃研究课题。PINNs在需要快速、近实时或实时数值预测的不同应用中提供了一种有前景的方法。岩土力学中的此类应用领域包括岩土工程设计优化、岩土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，尤其是在具有高时空复杂性问题中。本文以理想化的孔隙弹性问题作为示范实例，研究如何改善PINNs的训练。采用课程训练策略，将训练数据沿时间维度划分为区间，逐步训练PINN模型。我们发现，与在整个求解域上进行传统训练相比，采用课程训练的PINN模型所需训练时间几乎减少一半。就本文的具体实例而言，两种训练方法下预测解的质量均表现良好，但可以预期，对于更复杂的问题，课程训练方法有潜力提供更优的预测能力，这有待进一步研究。