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Machine learning method for the supplement, correction, and prediction of the nonlinear dynamics in pattern formation
Chen Y(陈一)1,2; Wu D(吴笛)1,2; Duan L(段俐)1,2; Kang Q(康琦)1,2
Corresponding AuthorDuan, Li(duanli@imech.ac.cn) ; Kang, Qi(kq@imech.ac.cn)
Source PublicationPHYSICS OF FLUIDS
2021-02-01
Volume33Issue:2Pages:17
ISSN1070-6631
AbstractThe pattern formation and spatial-temporal chaos are interesting issues in nonlinear dynamics. A novel model based on machine learning methods is designed to learn and imitate the pattern evolution in Benard-Marangoni convection (BM convection). There is a supercritical process, which is an inevitable and unique experimental phenomenon, on the way to chaos in BM convection. A single layer of fluid uniformly heated at the bottom is used as the experimental system. During the experiment, the temperature difference between top and bottom of the liquid layer is increased first to make the system enter the supercritical convection state and then decreased after a while; surface temperature distribution of the liquid layer is measured in real time with an infrared thermal imager, which visualized the formation and re-organization of cellular convection during the supercritical state. The temperature data are used as the material that meets the conditions of machine learning and then the machine learning method in charge of predicting the picture of temperature distribution that it never has seen before in two steps. The experimental data are used to train an auto-encoder model based on convolutional neural networks and an RNN-CNN joint model, in which the former is used for extracting low-dimensional features of the temperature field, and the latter is used for predicting evolution results of the low-dimensional features and recovering them back to the temperature field. The models have finally achieved the objectives of supplementing the missing experimental data and correcting actual experimental data by comparing the actual experimental results with the prediction results of the machine learning approach and theoretical analysis results. On the other hand, active exploration has been undertaken in predicting physical experimental results that have never happened before.
DOI10.1063/5.0036762
Indexed BySCI ; EI
Language英语
WOS IDWOS:000630504000005
WOS Research AreaMechanics ; Physics
WOS SubjectMechanics ; Physics, Fluids & Plasmas
Funding ProjectNational Natural Science Foundation of China[12032020] ; National Natural Science Foundation of China[12072354] ; Manned Space Program of China ; Strategic Priority Research Program on Space Science of the Chinese Academy of Sciences
Funding OrganizationNational Natural Science Foundation of China ; Manned Space Program of China ; Strategic Priority Research Program on Space Science of the Chinese Academy of Sciences
Classification一类/力学重要期刊
Ranking1
ContributorDuan, Li ; Kang, Qi
Citation statistics
Cited Times:5[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://dspace.imech.ac.cn/handle/311007/86175
Collection微重力重点实验室
Affiliation1.Chinese Acad Sci, Inst Mech, Natl Micrograv Lab, Beijing 100190, Peoples R China;
2.Univ Chinese Acad Sci, Sch Engn Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Chen Y,Wu D,Duan L,et al. Machine learning method for the supplement, correction, and prediction of the nonlinear dynamics in pattern formation[J]. PHYSICS OF FLUIDS,2021,33,2,:17.
APA 陈一,吴笛,段俐,&康琦.(2021).Machine learning method for the supplement, correction, and prediction of the nonlinear dynamics in pattern formation.PHYSICS OF FLUIDS,33(2),17.
MLA 陈一,et al."Machine learning method for the supplement, correction, and prediction of the nonlinear dynamics in pattern formation".PHYSICS OF FLUIDS 33.2(2021):17.
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