Maria Luisa is an enthusiastic independent engineer eager to develop strong knowledge and a talented researcher with excellent problem-solving, critical thinking and programming skills.
After obtaining a BSc and MSc with honors in Civil Engineering from University of Padua (Italy) and TU Delft (the Netherlands), respectively, Maria Luisa developed a strong background in geotechnical modeling and data analysis of results. For 5 years, she has worked as an employee at the Dutch indipendent research institute Deltares. There she has undertaken a wide range of applied research and innovative consultancy projects related to flood defences, which gave her the experience to effectively juggling multiple tasks and planning projects development. Among others, Maria Luisa mentored a Master student for 6 months, she coordinated the execution of innovative laboratory scale element tests, she published 8 papers and gave 5 international conferences presentations as first author. As a result of a 360-degrees feedback, colleagues showed appreciation for her pro-active approach and her sensitivity, defining Maria Luisa both as a collaborative team player and an independent worker.
Maria Luisa is an extremely curious person, who wants to make the world a better place. Besides participating to several volunteering experiences, she has a healthy and sustainable lifestyle, made of tennis, piano playing, movies, books and good friends.
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.