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Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, tra...

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Autores principales: Pawar, Suraj, San, Omer, Vedula, Prakash, Rasheed, Adil, Kvamsdal, Trond
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989982/
https://www.ncbi.nlm.nih.gov/pubmed/35393511
http://dx.doi.org/10.1038/s41598-022-09938-8
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author Pawar, Suraj
San, Omer
Vedula, Prakash
Rasheed, Adil
Kvamsdal, Trond
author_facet Pawar, Suraj
San, Omer
Vedula, Prakash
Rasheed, Adil
Kvamsdal, Trond
author_sort Pawar, Suraj
collection PubMed
description Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.
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spelling pubmed-89899822022-04-11 Multi-fidelity information fusion with concatenated neural networks Pawar, Suraj San, Omer Vedula, Prakash Rasheed, Adil Kvamsdal, Trond Sci Rep Article Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies. Nature Publishing Group UK 2022-04-07 /pmc/articles/PMC8989982/ /pubmed/35393511 http://dx.doi.org/10.1038/s41598-022-09938-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Pawar, Suraj
San, Omer
Vedula, Prakash
Rasheed, Adil
Kvamsdal, Trond
Multi-fidelity information fusion with concatenated neural networks
title Multi-fidelity information fusion with concatenated neural networks
title_full Multi-fidelity information fusion with concatenated neural networks
title_fullStr Multi-fidelity information fusion with concatenated neural networks
title_full_unstemmed Multi-fidelity information fusion with concatenated neural networks
title_short Multi-fidelity information fusion with concatenated neural networks
title_sort multi-fidelity information fusion with concatenated neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8989982/
https://www.ncbi.nlm.nih.gov/pubmed/35393511
http://dx.doi.org/10.1038/s41598-022-09938-8
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