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	Title	Type
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2		journal	1.488 Q1	206	1949	4138	95009	29287	4129	6.95	48.75	26.67
3		journal	1.262 Q1	34	262	236	14727	2180	236	8.98	56.21	16.48
4		journal	1.224 Q1	251	1059	4046	50739	22823	4045	5.46	47.91	24.17
5		journal	1.066 Q1	56	449	1728	22591	4693	1716	2.54	50.31	16.81
6		journal	1.050 Q1	203	3000	5425	150780	22962	5394	4.11	50.26	24.34
7		journal	1.049 Q1	6	40	48	2298	140	47	2.92	57.45	20.57
8		journal	1.035 Q1	72	1193	1969	50027	13906	1968	7.03	41.93	24.61
9		journal	0.966 Q1	83	148	446	8187	2935	445	6.52	55.32	18.74
10		journal	0.960 Q1	86	292	372	20629	1917	370	5.44	70.65	43.93
11		journal	0.956 Q1	66	47	123	2434	356	121	1.97	51.79	17.52
12		journal	0.939 Q1	140	248	726	12376	2925	725	3.84	49.90	18.18
13		journal	0.936 Q1	56	630	922	30416	5125	921	5.54	48.28	23.79
14		journal	0.882 Q1	13	22	69	931	381	69	5.52	42.32	17.28
15		journal	0.870 Q1	124	115	337	6436	1489	335	4.29	55.97	23.52
16		journal	0.865 Q1	55	69	266	2594	1292	262	4.51	37.59	20.59
17		journal	0.850 Q1	52	266	650	14162	2631	645	3.48	53.24	30.40
18		journal	0.848 Q1	142	196	686	8395	1767	677	2.36	42.83	14.42
19		journal	0.845 Q1	35	188	220	8848	852	214	3.51	47.06	23.08
20		journal	0.832 Q1	35	123	224	7400	1382	222	5.78	60.16	33.87
21		journal	0.784 Q1	18	34	85	1391	316	84	3.85	40.91	22.77
22		journal	0.763 Q1	41	41	101	2225	582	101	5.75	54.27	16.56
23		journal	0.731 Q1	137	209	558	9343	1954	554	3.14	44.70	24.15
24		journal	0.703 Q2	126	111	401	5102	1073	398	2.77	45.96	11.64
25		journal	0.570 Q2	42	71	214	4886	672	214	2.49	68.82	23.91
26		journal	0.569 Q2	17	49	59	2115	228	59	4.10	43.16	28.70
27		journal	0.537 Q2	79	227	410	8457	1023	398	2.54	37.26	16.99
28		journal	0.536 Q2	36	67	242	5679	1014	242	3.94	84.76	25.35
29		journal	0.524 Q2	35	43	123	2087	270	122	2.37	48.53	31.45
30		journal	0.522 Q2	46	254	1013	10864	3613	1009	3.56	42.77	19.10
31		journal	0.516 Q2	78	65	254	5509	774	252	2.71	84.75	34.12
32		journal	0.508 Q2	130	11085	34291	503877	107515	33727	2.92	45.46	32.43
33		journal	0.478 Q2	32	58	181	2631	438	167	2.03	45.36	31.20
34		journal	0.473 Q2	84	1139	3485	54643	10237	3446	2.89	47.97	27.43
35		journal	0.473 Q2	27	112	268	4148	577	257	2.14	37.04	23.64
36		journal	0.469 Q2	84	162	553	6925	1345	553	2.23	42.75	22.27
37		journal	0.461 Q2	71	85	315	3562	625	298	1.81	41.91	28.52
38		journal	0.437 Q2	19	23	91	677	158	91	1.81	29.43	14.49
39		journal	0.420 Q2	49	38	119	1830	242	119	2.13	48.16	39.70
40		journal	0.418 Q2	15	27	64	2419	143	64	2.13	89.59	31.82
41		journal	0.404 Q2	30	320	1090	14453	2169	1079	1.96	45.17	20.94
42		journal	0.404 Q2	28	0	41	0	113	41	2.17	0.00	0.00
43		journal	0.397 Q2	76	57	270	2311	403	246	1.34	40.54	17.09
44		journal	0.384 Q2	37	40	114	1598	203	110	1.75	39.95	17.83
45		journal	0.381 Q2	32	86	290	3136	526	285	1.92	36.47	20.68
46		journal	0.379 Q3	37	86	524	3101	867	521	1.70	36.06	21.11
47		journal	0.363 Q3	28	133	332	3041	260	332	0.83	22.86	26.09
48		journal	0.346 Q3	23	134	301	4455	677	293	2.23	33.25	22.60
49		journal	0.345 Q3	10	25	126	660	71	126	0.56	26.40	15.56
50		journal	0.337 Q3	36	25	62	1004	62	58	1.28	40.16	13.46

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Experimental Methods and their Applications to Fluid Flow

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Dynamic mode decomposition of numerical and experimental data.

Published online by Cambridge University Press:  01 July 2010

The description of coherent features of fluid flow is essential to our understanding of fluid-dynamical and transport processes. A method is introduced that is able to extract dynamic information from flow fields that are either generated by a (direct) numerical simulation or visualized/measured in a physical experiment. The extracted dynamic modes, which can be interpreted as a generalization of global stability modes, can be used to describe the underlying physical mechanisms captured in the data sequence or to project large-scale problems onto a dynamical system of significantly fewer degrees of freedom. The concentration on subdomains of the flow field where relevant dynamics is expected allows the dissection of a complex flow into regions of localized instability phenomena and further illustrates the flexibility of the method, as does the description of the dynamics within a spatial framework. Demonstrations of the method are presented consisting of a plane channel flow, flow over a two-dimensional cavity, wake flow behind a flexible membrane and a jet passing between two cylinders.

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• DOI: https://doi.org/10.1017/S0022112010001217

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• Perspective
• Published: 10 August 2023

The transformative potential of machine learning for experiments in fluid mechanics

• Ricardo Vinuesa   ORCID: orcid.org/0000-0001-6570-5499 1 , 2 ,
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Nature Reviews Physics volume  5 ,  pages 536–545 ( 2023 ) Cite this article

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The field of machine learning (ML) has rapidly advanced the state of the art in many fields of science and engineering, including experimental fluid dynamics, which is one of the original big-data disciplines. This Perspective article highlights several aspects of experimental fluid mechanics that stand to benefit from progress in ML, including augmenting the fidelity and quality of measurement techniques, improving experimental design and surrogate digital-twin models and enabling real-time estimation and control. In each case, we discuss recent success stories and ongoing challenges, along with caveats and limitations, and outline the potential for new avenues of ML-augmented and ML-enabled experimental fluid mechanics.

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Acknowledgements

The authors gratefully acknowledge valuable discussions with B. Noack early in the development of this Perspective article. R.V. acknowledges financial support from ERC grant no. 2021-CoG-101043998, DEEPCONTROL. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or the European Research Council. Neither the European Union nor the granting authority can be held responsible for them. S.L.B. acknowledges support from the National Science Foundation AI Institute in Dynamic Systems (grant no. 2112085). B.J.M. is grateful for the support of the U.S. ONR through a Vannevar Bush Faculty Fellowship, N00014-17-1-3022.

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Vinuesa, R., Brunton, S.L. & McKeon, B.J. The transformative potential of machine learning for experiments in fluid mechanics. Nat Rev Phys 5 , 536–545 (2023). https://doi.org/10.1038/s42254-023-00622-y

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