Imperial College London
Alternate

ProfessorSerafimKalliadasis

Faculty of Engineering, Department of Chemical Engineering

Prof in Engineering Science & Applied Mathematics
 
 
 
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Contact

 

+44 (0)20 7594 1373s.kalliadasis Website

 
 
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Assistant

 

Miss Jessica Baldock +44 (0)20 7594 5699

 
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Location

 

516ACE ExtensionSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Russo:2022:10.1109/TNNLS.2022.3210695,
author = {Russo, A and Duran-Olivencia, MA and Kevrekidis, IG and Kalliadasis, S},
doi = {10.1109/TNNLS.2022.3210695},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
pages = {1--13},
title = {Machine learning memory kernels as closure for non-Markovian stochastic processes},
url = {http://dx.doi.org/10.1109/TNNLS.2022.3210695},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Finding the dynamical law of observable quantities lies at the core of physics. Within the particular field of statistical mechanics, the generalized Langevin equation (GLE) comprises a general model for the evolution of observables covering a great deal of physical systems with many degrees of freedom and an inherently stochastic nature. Although formally exact, GLE brings its own great challenges. It depends on the complete history of the observables under scrutiny, as well as the microscopic degrees of freedom, all of which are often inaccessible. We show that these drawbacks can be overcome by adopting elements of machine learning from empirical data, in particular coupling a multilayer perceptron (MLP) with the formal structure of GLE and calibrating the MLP with the data. This yields a powerful computational tool capable of describing noisy complex systems beyond the realms of statistical mechanics. It is exemplified with a number of representative examples from different fields: from a single colloidal particle and particle chains in a thermal bath to climatology and finance, showing in all cases excellent agreement with the actual observable dynamics. The new framework offers an alternative perspective for the study of nonequilibrium processes opening also a new route for stochastic modeling.
AU  - Russo,A
AU  - Duran-Olivencia,MA
AU  - Kevrekidis,IG
AU  - Kalliadasis,S
DO  - 10.1109/TNNLS.2022.3210695
EP  - 13
PY  - 2022///
SN  - 1045-9227
SP  - 1
TI  - Machine learning memory kernels as closure for non-Markovian stochastic processes
T2  - IEEE Transactions on Neural Networks and Learning Systems
UR  - http://dx.doi.org/10.1109/TNNLS.2022.3210695
UR  - https://ieeexplore.ieee.org/document/9947343
UR  - https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5962385
UR  - http://hdl.handle.net/10044/1/100526
ER  -
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