%0 Journal Article %1 hemati2014dynamic %A Hemati, Maziar S. %A Williams, Matthew O. %A W.Rowley, Clarence %D 2014 %J Physics of Fluids %K 37a30-ergodic-theorems-spectral-theory-markov-operators DMD %N 111701 %R 10.1063/1.4901016 %T Dynamic mode decomposition for large and streaming datasets %U https://aip.scitation.org/doi/full/10.1063/1.4901016 %V 26 %X We formulate a low-storage method for performing dynamic mode decomposition that can be updated inexpensively as new data become available; this formulation allows dynamical information to be extracted from large datasets and data streams. We present two algorithms: the first is mathematically equivalent to a standard “batch-processed” formulation; the second introduces a compression step that maintains computational efficiency, while enhancing the ability to isolate pertinent dynamical information from noisy measurements. Both algorithms reliably capture dominant fluid dynamic behaviors, as demonstrated on cylinder wake data collected from both direct numerical simulations and particle image velocimetry experiments. We gratefully acknowledge Jessica Shang for providing access to the experimental PIV data for flow over a cylinder, as well as Scott T. M. Dawson and Jonathan H. Tu for sharing their insights on performing DMD analysis on such flows. M.O.W. acknowledges support from NSF DMS-1204783. C.W.R. and M.S.H. acknowledge support from AFOSR.

@article{hemati2014dynamic, abstract = {We formulate a low-storage method for performing dynamic mode decomposition that can be updated inexpensively as new data become available; this formulation allows dynamical information to be extracted from large datasets and data streams. We present two algorithms: the first is mathematically equivalent to a standard “batch-processed” formulation; the second introduces a compression step that maintains computational efficiency, while enhancing the ability to isolate pertinent dynamical information from noisy measurements. Both algorithms reliably capture dominant fluid dynamic behaviors, as demonstrated on cylinder wake data collected from both direct numerical simulations and particle image velocimetry experiments. We gratefully acknowledge Jessica Shang for providing access to the experimental PIV data for flow over a cylinder, as well as Scott T. M. Dawson and Jonathan H. Tu for sharing their insights on performing DMD analysis on such flows. M.O.W. acknowledges support from NSF DMS-1204783. C.W.R. and M.S.H. acknowledge support from AFOSR.}, added-at = {2020-10-21T17:02:43.000+0200}, author = {Hemati, Maziar S. and Williams, Matthew O. and W.Rowley, Clarence}, biburl = {https://www.bibsonomy.org/bibtex/2f027ac9c73ee1ceda749aa18fd4cfb78/gdmcbain}, doi = {10.1063/1.4901016}, interhash = {f09472424a55d34d76a5760ba5c57b91}, intrahash = {f027ac9c73ee1ceda749aa18fd4cfb78}, journal = {Physics of Fluids}, keywords = {37a30-ergodic-theorems-spectral-theory-markov-operators DMD}, number = 111701, timestamp = {2020-10-21T17:05:58.000+0200}, title = {Dynamic mode decomposition for large and streaming datasets}, url = {https://aip.scitation.org/doi/full/10.1063/1.4901016}, volume = 26, year = 2014 }