The duality between particle methods and artificial neural networks

Abstract The algorithm behind particle methods is extremely versatile and used in a variety of applications that range from molecular dynamics to astrophysics. For continuum mechanics applications, the concept of ‘particle’ can be generalized to include discrete portions of solid and liquid matter....

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Autores principales:	A. Alexiadis, M. J. H. Simmons, K. Stamatopoulos, H. K. Batchelor, I. Moulitsas
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ca0984051e2b46a9a399d40d1812f73e
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spelling	oai:doaj.org-article:ca0984051e2b46a9a399d40d1812f73e2021-12-02T18:51:28ZThe duality between particle methods and artificial neural networks10.1038/s41598-020-73329-02045-2322https://doaj.org/article/ca0984051e2b46a9a399d40d1812f73e2020-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-73329-0https://doaj.org/toc/2045-2322Abstract The algorithm behind particle methods is extremely versatile and used in a variety of applications that range from molecular dynamics to astrophysics. For continuum mechanics applications, the concept of ‘particle’ can be generalized to include discrete portions of solid and liquid matter. This study shows that it is possible to further extend the concept of ‘particle’ to include artificial neurons used in Artificial Intelligence. This produces a new class of computational methods based on ‘particle-neuron duals’ that combines the ability of computational particles to model physical systems and the ability of artificial neurons to learn from data. The method is validated with a multiphysics model of the intestine that autonomously learns how to coordinate its contractions to propel the luminal content forward (peristalsis). Training is achieved with Deep Reinforcement Learning. The particle-neuron duality has the advantage of extending particle methods to systems where the underlying physics is only partially known, but we have observations that allow us to empirically describe the missing features in terms of reward function. During the simulation, the model evolves autonomously adapting its response to the available observations, while remaining consistent with the known physics of the system.A. AlexiadisM. J. H. SimmonsK. StamatopoulosH. K. BatchelorI. MoulitsasNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q A. Alexiadis M. J. H. Simmons K. Stamatopoulos H. K. Batchelor I. Moulitsas The duality between particle methods and artificial neural networks
description	Abstract The algorithm behind particle methods is extremely versatile and used in a variety of applications that range from molecular dynamics to astrophysics. For continuum mechanics applications, the concept of ‘particle’ can be generalized to include discrete portions of solid and liquid matter. This study shows that it is possible to further extend the concept of ‘particle’ to include artificial neurons used in Artificial Intelligence. This produces a new class of computational methods based on ‘particle-neuron duals’ that combines the ability of computational particles to model physical systems and the ability of artificial neurons to learn from data. The method is validated with a multiphysics model of the intestine that autonomously learns how to coordinate its contractions to propel the luminal content forward (peristalsis). Training is achieved with Deep Reinforcement Learning. The particle-neuron duality has the advantage of extending particle methods to systems where the underlying physics is only partially known, but we have observations that allow us to empirically describe the missing features in terms of reward function. During the simulation, the model evolves autonomously adapting its response to the available observations, while remaining consistent with the known physics of the system.
format	article
author	A. Alexiadis M. J. H. Simmons K. Stamatopoulos H. K. Batchelor I. Moulitsas
author_facet	A. Alexiadis M. J. H. Simmons K. Stamatopoulos H. K. Batchelor I. Moulitsas
author_sort	A. Alexiadis
title	The duality between particle methods and artificial neural networks
title_short	The duality between particle methods and artificial neural networks
title_full	The duality between particle methods and artificial neural networks
title_fullStr	The duality between particle methods and artificial neural networks
title_full_unstemmed	The duality between particle methods and artificial neural networks
title_sort	duality between particle methods and artificial neural networks
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ca0984051e2b46a9a399d40d1812f73e
work_keys_str_mv	AT aalexiadis thedualitybetweenparticlemethodsandartificialneuralnetworks AT mjhsimmons thedualitybetweenparticlemethodsandartificialneuralnetworks AT kstamatopoulos thedualitybetweenparticlemethodsandartificialneuralnetworks AT hkbatchelor thedualitybetweenparticlemethodsandartificialneuralnetworks AT imoulitsas thedualitybetweenparticlemethodsandartificialneuralnetworks AT aalexiadis dualitybetweenparticlemethodsandartificialneuralnetworks AT mjhsimmons dualitybetweenparticlemethodsandartificialneuralnetworks AT kstamatopoulos dualitybetweenparticlemethodsandartificialneuralnetworks AT hkbatchelor dualitybetweenparticlemethodsandartificialneuralnetworks AT imoulitsas dualitybetweenparticlemethodsandartificialneuralnetworks
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The duality between particle methods and artificial neural networks

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