The Library
Physics-informed neural networks as surrogate models of hydrodynamic simulators
Tools
Donnelly, James, Daneshkhah, Alireza and Abolfathi, Soroush (2024) Physics-informed neural networks as surrogate models of hydrodynamic simulators. Science of The Total Environment, 912 . 168814. doi:10.1016/j.scitotenv.2023.168814 ISSN 0048-9697.
|
PDF
1-s2.0-S0048969723074430-main.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (20Mb) | Preview |
Official URL: https://doi.org/10.1016/j.scitotenv.2023.168814
Abstract
In response to growing concerns surrounding the relationship between climate change and escalating flood risk, there is an increasing urgency to develop precise and rapid flood prediction models. Although high-resolution flood simulations have made notable advancements, they remain computationally expensive, underscoring the need for efficient machine learning surrogate models. As a result of sparse empirical observation and expensive data collection, there is a growing need for the models to perform effectively in ‘small-data’ contexts, a characteristic typical of many scientific problems. This research combines the latest developments in surrogate modelling and physics-informed machine learning to propose a novel Physics-Informed Neural Network-based surrogate model for hydrodynamic simulators governed by Shallow Water Equations. The proposed method incorporates physics-based prior information into the neural network structure by encoding the conservation of mass into the model without relying on calculating continuous derivatives in the loss function. The method is demonstrated for a high-resolution inland flood simulation model and a large-scale regional tidal model. The proposed method outperforms the existing state-of-the-art data-driven approaches by up to 25 %. This research demonstrates the benefits and robustness of physics-informed approaches in surrogate modelling for flood and hydroclimatic modelling problems.
Item Type: | Journal Article | ||||||||
---|---|---|---|---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TC Hydraulic engineering. Ocean engineering |
||||||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > Engineering | ||||||||
Library of Congress Subject Headings (LCSH): | Hydrodynamics -- Mathematical models, Machine learning, Flood control -- Computer programs | ||||||||
Journal or Publication Title: | Science of The Total Environment | ||||||||
Publisher: | Elsevier Science BV | ||||||||
ISSN: | 0048-9697 | ||||||||
Official Date: | 20 February 2024 | ||||||||
Dates: |
|
||||||||
Volume: | 912 | ||||||||
Article Number: | 168814 | ||||||||
DOI: | 10.1016/j.scitotenv.2023.168814 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 4 December 2023 | ||||||||
Date of first compliant Open Access: | 8 December 2023 |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year