scholarly journals Kalman Filter Dynamic Mode Decomposition for Data Assimilation

2019 ◽  
Vol 21 (4) ◽  
pp. 73-74
Author(s):  
Taku Nonomura
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

scholarly journals Dynamic mode decomposition using a Kalman filter for parameter estimation

AIP Advances ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. 105106 ◽  
Author(s):  
Taku Nonomura ◽  
Hisaichi Shibata ◽  
Ryoji Takaki
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition

scholarly journals Time-resolved denoising using model order reduction, dynamic mode decomposition, and kalman filter and smoother

2020 ◽  
Vol 7 (2) ◽  
pp. 469-487
Author(s):  
Mojtaba F. Fathi ◽  
◽  
Ahmadreza Baghaie ◽  
Ali Bakhshinejad ◽  
Raphael H. Sacho ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Model Order Reduction ◽  
Order Reduction ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Model Order ◽  
Time Resolved ◽  
Mode Decomposition

scholarly journals Reduced order modeling for stochastic prediction and data assimilation onboard autonomous platforms at sea

2021 ◽  
Author(s):  
◽  
Jacob P. Heuss
Keyword(s):  
Data Assimilation ◽  
Reduced Order Modeling ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Observation Data ◽  
Computationally Efficient ◽  
Reduced Order ◽  
Mode Decomposition ◽  
Combine Uncertainty ◽  
Ocean Environment

There are many significant challenges for unmanned autonomous platforms at sea including predicting the likely scenarios for the ocean environment, quantifying regional uncertainties, and updating forecasts of the evolving dynamics using their observations. Due to the operational constraints such as onboard power, memory, bandwidth, and space limitations, efficient adaptive reduced order models (ROMs) are needed for onboard predictions. In the first part, several reduced order modeling schemes for regional ocean forecasting onboard autonomous platforms at sea are described, investigated, and evaluated. We find that Dynamic Mode Decomposition (DMD), a data-driven dimensionality reduction algorithm, can be used for accurate predictions for short periods in ocean environments. We evaluate DMD methods for ocean PE simulations by comparing and testing several schemes including domain splitting, adjusting training size, and utilizing 3D inputs. Three new approaches that combine uncertainty with DMD are also investigated and found to produce practical and accurate results, especially if we employ either an ensemble of DMD forecasts or the DMD of an ensemble of forecasts. We also demonstrate some results from projecting / compressing high-fidelity forecasts using schemes such as POD projection and K-SVD for sparse representation due to showing promise for distributing forecasts efficiently to remote vehicles. In the second part, we combine DMD methods with the GMM-DO filter to produce DMD forecasts with Bayesian data assimilation that can quickly and efficiently be computed onboard an autonomous platform. We compare the accuracy of our results to traditional DMD forecasts and DMD with Ensemble Kalman Filter (EnKF) forecast results and show that in Root Mean Square Error (RMSE) sense as well as error field sense, that the DMD with GMM-DO errors are smaller and the errors grow slower in time than the other mentioned schemes. We also showcase the DMD of the ensemble method with GMM-DO. We conclude that due to its accurate and computationally efficient results, it could be readily applied onboard autonomous platforms. Overall, our contributions developed and integrated stochastic DMD forecasts and efficient Bayesian GMM-DO updates of the DMD state and parameters, learning from the limited gappy observation data sets.

Experimental Measurement of In-Plane Rolling Nonpneumatic Tire Vibrations Using High-Speed Imaging

2019 ◽  
Vol 47 (3) ◽  
pp. 196-210
Author(s):  
Meghashyam Panyam ◽  
Beshah Ayalew ◽  
Timothy Rhyne ◽  
Steve Cron ◽  
John Adcox
Keyword(s):  
High Speed ◽  
Image Correlation ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
High Speed Imaging ◽  
Correlation Algorithm ◽  
Mode Decomposition ◽  
Series Of Experiments ◽  
Plane Deformations ◽  
Dominant Frequencies

ABSTRACT This article presents a novel experimental technique for measuring in-plane deformations and vibration modes of a rotating nonpneumatic tire subjected to obstacle impacts. The tire was mounted on a modified quarter-car test rig, which was built around one of the drums of a 500-horse power chassis dynamometer at Clemson University's International Center for Automotive Research. A series of experiments were conducted using a high-speed camera to capture the event of the rotating tire coming into contact with a cleat attached to the surface of the drum. The resulting video was processed using a two-dimensional digital image correlation algorithm to obtain in-plane radial and tangential deformation fields of the tire. The dynamic mode decomposition algorithm was implemented on the deformation fields to extract the dominant frequencies that were excited in the tire upon contact with the cleat. It was observed that the deformations and the modal frequencies estimated using this method were within a reasonable range of expected values. In general, the results indicate that the method used in this study can be a useful tool in measuring in-plane deformations of rolling tires without the need for additional sensors and wiring.

Oscillatory flow around a vertical wall-mounted cylinder: Dynamic mode decomposition

2021 ◽  
Vol 33 (2) ◽  
pp. 025113
Author(s):  
H. K. Jang ◽  
C. E. Ozdemir ◽  
J.-H. Liang ◽  
M. Tyagi
Keyword(s):  
Oscillatory Flow ◽  
Vertical Wall ◽  
Dynamic Mode Decomposition ◽  
Dynamic Mode ◽  
Mode Decomposition
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