Robust Dynamic Mode Decomposition

Amir Abolmasoumi, Marcos Netto, Lamine Mili

Research output: Contribution to journalArticlepeer-review

8 Scopus Citations


This paper develops a robust dynamic mode decomposition (RDMD) method endowed with statistical and numerical robustness. Statistical robustness ensures estimation efficiency at the Gaussian and non-Gaussian probability distributions, including heavy-tailed distributions. The proposed RDMD is statistically robust because the outliers in the data set are flagged via projection statistics and suppressed using a Schweppe-type Huber generalized maximum-likelihood estimator that minimizes a convex Huber cost function. The latter is solved using the iteratively reweighted least-squares algorithm that is known to exhibit an excellent convergence property and numerical stability than the Newton algorithms. Several numerical simulations using canonical models of dynamical systems demonstrate the excellent performance of the proposed RDMD method. The results reveal that it outperforms several other methods proposed in the literature.
Original languageAmerican English
Pages (from-to)65473-65484
Number of pages12
JournalIEEE Access
StatePublished - 2022

NREL Publication Number

  • NREL/JA-5D00-79972


  • dynamic mode decomposition
  • outlier detection
  • robust estimation
  • robust regression
  • robust statistics


Dive into the research topics of 'Robust Dynamic Mode Decomposition'. Together they form a unique fingerprint.

Cite this