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METHODS article

Front. Complex Syst.
Sec. Complex Physical Systems
Volume 2 - 2024 | doi: 10.3389/fcpxs.2024.1508091

A priori physical information to aid generalization capabilities of Neural Networks for hydraulic modeling

Provisionally accepted

The final, formatted version of the article will be published soon.

    The application of Neural Networks to river hydraulics and flood mapping is fledgling, despite the field suffering from data scarcity, a challenge for machine learning techniques. Consequently, many purely data-driven Neural Networks have shown limited capabilities when tasked with predicting new scenarios. In this work, we propose introducing physical information into the training phase in the form of a regularization term. Whereas this idea is formally borrowed from Physics-Informed Neural Networks, the proposed methodology does not necessarily resort to PDEs, making it suitable for scenarios with significant epistemic uncertainties, such as river hydraulics. The method enriches the information content of the dataset and appears highly versatile. It shows improved predictive capabilities for a highly controllable, synthetic hydraulic problem, even when extrapolating beyond the boundaries of the training dataset and in datascarce scenarios. Therefore, our study lays the groundwork for future employment on real datasets from complex applications.

    Keywords: neural networks, physical training strategies, river hydraulics, Hydraulic modeling, generalization

    Received: 08 Oct 2024; Accepted: 03 Dec 2024.

    Copyright: © 2024 GUGLIELMO, Montessori, Tucny, La Rocca and Prestininzi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: GIANMARCO GUGLIELMO, Roma Tre University, Rome, Italy

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.