Uploaded April 2024 | Updated September 2026, 2 weeks ago
Video by Tanner Harms, based on "Lagrangian Gradient Regression for the Detection of Coherent Structures from Sparse Trajectory Data"
by Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon
arxiv.org/abs/2310.10994
The method of Lagrangian Coherent Structures (LCS) uses particle trajectories in fluid flows to identify coherent structures that govern the behavior of the flow. The typical methods employed to identify LCS rely on a dense grid of numerical tracers which are seeded onto the pre-computed vector fields and advected through time and space. However, in many systems, dense flow field information is not available, and researchers must perform their analyses on a sparse set of tracers that already exist in the flow. For example, if we wish our flow observer to autonomously make decisions based on the flow field information, then the computational expense of the normal LCS identification pipeline is too costly to use. It will need to use only the trajectories of the tracers that it sees in the flow. Motivated by the desire to study flow fields autonomously, this video shows how sparse trajectory data can be leveraged to identify key flow quantities including the velocity gradient, Finite-Time Lyapunov Exponent (FTLE), and Lagrangian-Averaged Vorticity Deviation (LAVD), which are often used to identify LCS.
Key links:
youtube.com/watch?v=lveOu7jLNh0
arxiv.org/abs/2310.10994
pubs.aip.org/aip/cha/article-abstract/20/1/017503/280647/Fast-computation-of-finite-time-Lyapunov-exponent?redirectedFrom=fulltext
amazon.com/Transport-Barriers-Coherent-Structures-Flow/dp/1009225170/ref=sr_1_1?crid=2B4GREV4UGIC8&dib=eyJ2IjoiMSJ9.qjMjRA8SBCd-3Ae8SG6jGbX6bldhU7BK3jvLRjmuEKxcPr_qK4lUpwnrmT3A280AdIFVFal92uVoxHmYI1jpY2XQlpsIV-rD_mGA8h22VZ07bINHllI1OO8VkK2N1FAoJ6y-Z-vR9U4FVUE_mu4GLaWBVmqofJuNABo_uyrK_jDrWoQa4Qcr3opSl-6t7EadHjHDXccJ-LbLpO97oB-pfz3HWVjbBj2znEH-2cZKYYw.msqHpb8gaubQ_rfWE_8DaBpRAAPjVHOJX5J4KMt4Ia8&dib_tag=se&keywords=george+haller&qid=1708992219&sprefix=george+hall%2Caps%2C359&sr=8-1
pubs.aip.org/aip/cha/article/25/9/097617/134953
Video by Tanner Harms, based on "Lagrangian Gradient Regression for the Detection of Coherent Structures from Sparse Trajectory Data"
by Tanner D. Harms, Steven L. Brunton, Beverley J. McKeon
arxiv.org/abs/2310.10994
The method of Lagrangian Coherent Structures (LCS) uses particle trajectories in fluid flows to identify coherent structures that govern the behavior of the flow. The typical methods employed to identify LCS rely on a dense grid of numerical tracers which are seeded onto the pre-computed vector fields and advected through time and space. However, in many systems, dense flow field information is not available, and researchers must perform their analyses on a sparse set of tracers that already exist in the flow. For example, if we wish our flow observer to autonomously make decisions based on the flow field information, then the computational expense of the normal LCS identification pipeline is too costly to use. It will need to use only the trajectories of the tracers that it sees in the flow. Motivated by the desire to study flow fields autonomously, this video shows how sparse trajectory data can be leveraged to identify key flow quantities including the velocity gradient, Finite-Time Lyapunov Exponent (FTLE), and Lagrangian-Averaged Vorticity Deviation (LAVD), which are often used to identify LCS.
Key links:
youtube.com/watch?v=lveOu7jLNh0
arxiv.org/abs/2310.10994
pubs.aip.org/aip/cha/article-abstract/20/1/017503/280647/Fast-computation-of-finite-time-Lyapunov-exponent?redirectedFrom=fulltext
amazon.com/Transport-Barriers-Coherent-Structures-Flow/dp/1009225170/ref=sr_1_1?crid=2B4GREV4UGIC8&dib=eyJ2IjoiMSJ9.qjMjRA8SBCd-3Ae8SG6jGbX6bldhU7BK3jvLRjmuEKxcPr_qK4lUpwnrmT3A280AdIFVFal92uVoxHmYI1jpY2XQlpsIV-rD_mGA8h22VZ07bINHllI1OO8VkK2N1FAoJ6y-Z-vR9U4FVUE_mu4GLaWBVmqofJuNABo_uyrK_jDrWoQa4Qcr3opSl-6t7EadHjHDXccJ-LbLpO97oB-pfz3HWVjbBj2znEH-2cZKYYw.msqHpb8gaubQ_rfWE_8DaBpRAAPjVHOJX5J4KMt4Ia8&dib_tag=se&keywords=george+haller&qid=1708992219&sprefix=george+hall%2Caps%2C359&sr=8-1
pubs.aip.org/aip/cha/article/25/9/097617/134953
![[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition]](https://i.ytimg.com/vi/xSZb2UmpzO8/mqdefault.jpg "[5/8] Control for Societal-Scale Challenges: Road Map 2030 [Technology, Validation, and Transition]
This video explores the need for validation and benchmark problems to transition technology to broad industrial applications. This follows Chapter 5 of the Control for Societal-Scale Challenges: Road Map 2030 document.
https://www.ieeecss.org/control-societal-scale-challenges-road-map-2030
The production of this video was supported by the IFAC.
Roadmap Abstract: The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.
Edited by: Anuradha M. Annaswamy, Karl H. Johansson, and George J. Pappas
Authors: Andrew Alleyne, Frank Allgöwer, Aaron D. Ames, Saurabh Amin, James Anderson, Anuradha M. Annaswamy, Panos J. Antsaklis, Neda Bagheri, Hamsa Balakrishnan, Bassam Bamieh, John Baras, Margret Bauer, Alexandre Bayen, Paul Bogdan, Steven L. Brunton, Francesco Bullo, Etienne Burdet, Joel Burdick, Laurent Burlion, Carlos Canudas de Wit, Ming Cao, Christos G. Cassandras, Aranya Chakrabortty, Giacomo Como, Marie Csete, Fabrizio Dabbene, Munther Dahleh, Amritam Das, Eyal Dassau, Claudio De Persis, Mario di Bernardo, Stefano Di Cairano, Dimos V. Dimarogonas, Florian Dörfler, John C. Doyle, Francis J. Doyle III, Anca Dragan, Magnus Egerstedt, Johan Eker, Sarah Fay, Dimitar Filev, Angela Fontan, Elisa Franco, Masayuki Fujita, Mario Garcia-Sanz, Dennice Gayme, Wilhelmus P.M.H. Heemels, João P. Hespanha, Sandra Hirche, Anette Hosoi, Jonathan P. How, Gabriela Hug, Marija Ilić, Hideaki Ishii, Ali Jadbabaie, Matin Jafarian, Samuel Qing-Shan Jia, Tor Arne Johansen, Karl H. Johansson, Dalton Jones, Mustafa Khammash, Pramod Khargonekar, Mykel J. Kochenderfer, Andreas Krause, Anthony Kuh, Dana Kulić, Françoise Lamnabhi-Lagarrigue, Naomi E. Leonard, Frederick Leve, Na Li, Steven Low, John Lygeros, Iven Mareels, Sonia Martinez, Nikolai Matni, Tommaso Menara, Katja Mombaur, Kevin Moore, Richard Murray, Toru Namerikawa, Angelia Nedich, Sandeep Neema, Mariana Netto, Timothy O’Leary, Marcia K. O’Malley, Lucy Y. Pao, Antonis Papachristodoulou, George J. Pappas, Philip E. Paré, Thomas Parisini, Fabio Pasqualetti, Marco Pavone, Akshay Rajhans, Gireeja Ranade, Anders Rantzer, Lillian Ratliff, J. Anthony Rossiter, Dorsa Sadigh, Tariq Samad, Henrik Sandberg, Sri Sarma, Luca Schenato, Jacquelien Scherpen, Angela Schoellig, Rodolphe Sepulchre, Jeff Shamma, Robert Shorten, Bruno Sinopoli, Koushil Sreenath, Jakob Stoustrup, Jing Sun, Paulo Tabuada, Emma Tegling, Dawn Tilbury, Claire J. Tomlin, Jana Tumova, Kevin Wise, Dan Work, Junaid Zafar, Melanie Zeilinger
This video was produced at the University of Washington")
![Neural Implicit Flow (NIF) [Physics Informed Machine Learning]](https://i.ytimg.com/vi/y-s1oECkbuU/mqdefault.jpg "Neural Implicit Flow (NIF) [Physics Informed Machine Learning]
This video was produced at the University of Washington, and we acknowledge funding support from the Boeing Company
%%% CHAPTERS %%%
00:00 Intro
01:25 Underlying Concept
// 02:32 Example Problem
04:36 Example Application: Turbulent Data Compression
06:23 Example Application: Sparse Sensor Placement
08:09 NIF is Mesh Agnostic
10:30 Results/Benchmark Data
// 11:00 Growing Vortices/ Cool Pictures
11:40 Shape Net Architectures
12:30 Outro")
