Application of physics informed neural networks to compositional modeling-Reference-Cited by-同舟云学术

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Application of physics informed neural networks to compositional modeling

Published:2022-04 Issue: Volume:211 Page:110175
ISSN:0920-4105
Container-title:Journal of Petroleum Science and Engineering
language:en
Short-container-title:Journal of Petroleum Science and Engineering

Author:

Ihunde Thelma Anizia,Olorode Olufemi

Publisher

Elsevier BV

Subject

Geotechnical Engineering and Engineering Geology,Fuel Technology

Reference43 articles.

1. Comparison of two methods for speeding up flash calculations in compositional simulations;Belkadi,2011

2. Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling;Beucler,2019

3. Bagging predictors;Breiman;Mach. Learn.,1996

4. An equation of state compositional model;Coats;Soc. Petrol. Eng. J.,1980

5. Physics-guided Neural Networks (Pgnn): an Application in Lake Temperature Modeling;Daw,2021

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2. Development of Steady-State and Dynamic Mass and Energy Constrained Neural Networks for Distributed Chemical Systems Using Noisy Transient Data;Industrial & Engineering Chemistry Research;2024-08-02

3. On the development of steady-state and dynamic mass-constrained neural networks using noisy transient data;Computers & Chemical Engineering;2024-08

4. Development of Steady-State and Dynamic Mass-Energy Constrained Neural Networks using Noisy Transient Data;Systems and Control Transactions;2024-07-09

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