The duality between particle methods and artificial neural networks release_r2ico3gsb5bpzeqnewa7fdihci

by A. Alexiadis, M. J. H. Simmons, K. Stamatopoulos, H. K. Batchelor, I. Moulitsas

Published in Scientific Reports by Springer Science and Business Media LLC.

2020   Volume 10, Issue 1, p16247

Abstract

<jats:title>Abstract</jats:title> 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.
In application/xml+jats format

Archived Files and Locations

application/pdf  1.5 MB
file_6jd54kj3hvehvjmopjc3w3ef6m
pure-oai.bham.ac.uk (web)
web.archive.org (webarchive)
application/pdf  1.5 MB
file_fndpso7invcvhhvdbd2vudtyiy
dspace.lib.cranfield.ac.uk (web)
web.archive.org (webarchive)
application/pdf  1.5 MB
file_vw7zmryhr5bbxlogvkmp2v2ly4
research.birmingham.ac.uk (web)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article-journal
Stage   published
Date   2020-10-01
Language   en ?
Container Metadata
Open Access Publication
In DOAJ
In Keepers Registry
ISSN-L:  2045-2322
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: f57dd293-79bc-4643-b439-640d829a32a6
API URL: JSON