Physics informed deep learning for groundwater prediction

人工智能在过去十年中引起了土木工程师的极大关注，特别是人工神经网络（ANN），它可以提供一种灵活的数学结构，能够识别输入和输出数据集之间的复杂非线性关系。然而，传统上，人工神经网络是使用具有简单损失函数的输入和输出数据集进行训练的，这种方法没有将物理系统知识纳入学习过程，同时需要大量的数据，这些数据要么成本高昂，要么不可用。另一方面，概念和计算建模在过去几十年中持续快速发展，因为它基于基本的本构物理方程，能够提供准确的分析和预测，但它受到与计算性能相关的问题的困扰，这可能会阻碍其使用。该项目将基础物理融入深度学习过程的训练过程，结合计算建模和深度神经网络，建立新一代物理学深度学习方法，可以准确快速地估计地下水系统响应。这项工作的成果将非常有益于气候变化情景下的岩土工程和地下水保护，比如干旱的预测。

Artificial Intelligence have attracted considerable attention from Civil Engineers over the last decade, especially Artificial Neural Networks (ANN) which can provide a flexible mathematical structure capable of identifying complex nonlinear relationships between input and output data sets. However, traditionally, ANNs have been trained using input and output datasets with simple loss functions, which incorporates no physical system knowledge into the learning process, while requiring huge amounts of data, which are either costly to produce or unavailable. Alternatively, conceptual and computational modelling has continued rapid development in the past decades as it is based on fundamental constitutive physical equations and able to provide accurate analysis and prediction, however, it suffers from problems associated with computational performance, which can hinder its usage.This project will integrate fundamental physics into the training process of deep learning processes, engaging computational modelling and deep neural network, and establish a new generation of physics informed deep learning methods, which can provide accurate and quick estimates of groundwater system response.The outcome of this work will highly benefit to geotechnical engineering and groundwater protection in climate changing scenarios, for instance in the forecasting of drought.