Bayesian Optimization of Bose-Einstein Condensates

Abstract Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-driven approaches to faithfully model physical phenomena using scattered, noisy observations from coarse-grained g...

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Autores principales:	Tamil Arasan Bakthavatchalam, Suriyadeepan Ramamoorthy, Malaikannan Sankarasubbu, Radha Ramaswamy, Vijayalakshmi Sethuraman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/e2e87a1639044c67a46590d351cc9e6e
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spelling	oai:doaj.org-article:e2e87a1639044c67a46590d351cc9e6e2021-12-02T11:35:57ZBayesian Optimization of Bose-Einstein Condensates10.1038/s41598-021-84336-02045-2322https://doaj.org/article/e2e87a1639044c67a46590d351cc9e6e2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-84336-0https://doaj.org/toc/2045-2322Abstract Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-driven approaches to faithfully model physical phenomena using scattered, noisy observations from coarse-grained grid-based simulations. In this paper, we investigate data-driven modelling of Bose-Einstein Condensates (BECs). In particular, we use Gaussian Processes (GPs) to model the ground state wave function of BECs as a function of scattering parameters from the dimensionless Gross Pitaveskii Equation (GPE). Experimental results illustrate the ability of GPs to accurately reproduce ground state wave functions using a limited number of data points from simulations. Consistent performance across different configurations of BECs, namely Scalar and Vectorial BECs generated under different potentials, including harmonic, double well and optical lattice potentials pronounces the versatility of our method. Comparison with existing data-driven models indicates that our model achieves similar accuracy with only a small fraction ( $$\frac{1}{50}$$ 1 50 th) of data points used by existing methods, in addition to modelling uncertainty from data. When used as a simulator post-training, our model generates ground state wave functions $$36 \times $$ 36 × faster than Trotter Suzuki, a numerical approximation technique that uses Imaginary time evolution. Our method is quite general; with minor changes it can be applied to similar quantum many-body problems.Tamil Arasan BakthavatchalamSuriyadeepan RamamoorthyMalaikannan SankarasubbuRadha RamaswamyVijayalakshmi SethuramanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tamil Arasan Bakthavatchalam Suriyadeepan Ramamoorthy Malaikannan Sankarasubbu Radha Ramaswamy Vijayalakshmi Sethuraman Bayesian Optimization of Bose-Einstein Condensates
description	Abstract Machine Learning methods are emerging as faster and efficient alternatives to numerical simulation techniques. The field of Scientific Computing has started adopting these data-driven approaches to faithfully model physical phenomena using scattered, noisy observations from coarse-grained grid-based simulations. In this paper, we investigate data-driven modelling of Bose-Einstein Condensates (BECs). In particular, we use Gaussian Processes (GPs) to model the ground state wave function of BECs as a function of scattering parameters from the dimensionless Gross Pitaveskii Equation (GPE). Experimental results illustrate the ability of GPs to accurately reproduce ground state wave functions using a limited number of data points from simulations. Consistent performance across different configurations of BECs, namely Scalar and Vectorial BECs generated under different potentials, including harmonic, double well and optical lattice potentials pronounces the versatility of our method. Comparison with existing data-driven models indicates that our model achieves similar accuracy with only a small fraction ( $$\frac{1}{50}$$ 1 50 th) of data points used by existing methods, in addition to modelling uncertainty from data. When used as a simulator post-training, our model generates ground state wave functions $$36 \times $$ 36 × faster than Trotter Suzuki, a numerical approximation technique that uses Imaginary time evolution. Our method is quite general; with minor changes it can be applied to similar quantum many-body problems.
format	article
author	Tamil Arasan Bakthavatchalam Suriyadeepan Ramamoorthy Malaikannan Sankarasubbu Radha Ramaswamy Vijayalakshmi Sethuraman
author_facet	Tamil Arasan Bakthavatchalam Suriyadeepan Ramamoorthy Malaikannan Sankarasubbu Radha Ramaswamy Vijayalakshmi Sethuraman
author_sort	Tamil Arasan Bakthavatchalam
title	Bayesian Optimization of Bose-Einstein Condensates
title_short	Bayesian Optimization of Bose-Einstein Condensates
title_full	Bayesian Optimization of Bose-Einstein Condensates
title_fullStr	Bayesian Optimization of Bose-Einstein Condensates
title_full_unstemmed	Bayesian Optimization of Bose-Einstein Condensates
title_sort	bayesian optimization of bose-einstein condensates
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/e2e87a1639044c67a46590d351cc9e6e
work_keys_str_mv	AT tamilarasanbakthavatchalam bayesianoptimizationofboseeinsteincondensates AT suriyadeepanramamoorthy bayesianoptimizationofboseeinsteincondensates AT malaikannansankarasubbu bayesianoptimizationofboseeinsteincondensates AT radharamaswamy bayesianoptimizationofboseeinsteincondensates AT vijayalakshmisethuraman bayesianoptimizationofboseeinsteincondensates
_version_	1718395853557727232

Bayesian Optimization of Bose-Einstein Condensates

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