Summary
Artificial Intelligence has 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, tested by real experiments/modelling to establish a new generation of physics informed deep learning methods, which can provide accurate and quick estimates of geotechnical and geoenvironmental systems response.
The outcome of this work will highly benefit Geotechnical & Environmental engineering in climate-changing scenarios, for instance in the forecasting of highly nonlinear flooding/drought influence on the resilience of the geosystem.