Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network-Reference-Cited by-同舟云学术

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Hybrid Chemical and Data-Driven Model for Stiff Chemical Kinetics Using a Physics-Informed Neural Network

Published:2024-09-03 Issue: Volume:55 Page:40
ISSN:
Container-title:The 3rd International Joint Conference on Water Distribution Systems Analysis & Computing and Control for the Water Industry (WDSA/CCWI 2024)
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Author:

Frankel Matthew¹,De Florio Mario²^ORCID,Schiassi Enrico³,Sela Lina¹^ORCID

Affiliation:

1. Fariborz Maseeh Department of Civil Architectural and Environmental Engineering, The University of Texas at Austin, Austin, TX 78712, USA

2. Division of Applied Mathematics, Brown University, Providence, RI 02906, USA

3. Department of Industrial Engineering, University of Bologna, 40126 Bologna, Italy

Publisher

MDPI

Link

https://www.mdpi.com/2673-4591/69/1/40/pdf

Reference9 articles.

1. Updated reaction pathway for dichloramine decomposition: Formation of reactive nitrogen species and N-nitrosodimethylamine;Pham;Environ. Sci. Technol.,2021

2. Forecasting point-of-consumption chlorine residual in refugee settlements using ensembles of artificial neural networks;Khan;NJP Clean Water,2021

3. Physics-informed machine learning;Karniadakis;Nat. Rev. Phys.,2021

4. Physics-informed neural networks and functional interpolation for stiff chemical kinetics;Schiassi;Chaos,2022

5. American Water Works Association (2024, March 12). 2017 Water Utility Disinfection Survey Report. Available online: https://www.awwa.org/Portals/0/AWWA/ETS/Resources/2017DisinfectionSurveyReport.pdf?ver=2018-12-21-163548-830.

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