Steven L. Brunton

University of Washington

201Publications

27H-index

3,424Citations

Publications 209

Newest

Modal analysis techniques are used to identify patterns and develop reduced-order models in a variety of fluid applications. However, experimentally acquired flow fields may be corrupted with incorrect and missing entries, which may degrade modal decomposition. Here we use robust principal component analysis (RPCA) to improve the quality of flow field data by leveraging global coherent structures to identify and replace spurious data points. RPCA is a robust variant of principal component analys...

Time delays due to signal latency, computational complexity, and sensor-denied environments, pose a critical challenge in both engineered and biological control systems. In this work, we investigate biologically inspired strategies to develop precisely timed feedforward control laws for engineered systems with large time delays. We demonstrate this approach on the nonlinear pendulum with partially denied observations, so that it is only possible to measure the state of the system near the uprigh...

#1Samuel H. Rudy (UW: University of Washington)H-Index: 4

#2Steven L. Brunton (UW: University of Washington)H-Index: 27

Last.J. Nathan Kutz (UW: University of Washington)H-Index: 38

view all 3 authors...

Abstract The analysis of high-dimensional dynamical systems generally requires the integration of simulation data with experimental measurements. Experimental data often has substantial amounts of measurement noise that compromises the ability to produce accurate dimensionality reduction, parameter estimation, reduced order models, and/or balanced models for control. Data assimilation attempts to overcome the deleterious effects of noise by producing a set of algorithms for state estimation from...

We demonstrate the effective use of randomized methods for linear algebra to perform network-based analysis of complex vortical flows. Network theoretic approaches can reveal the connectivity structures among a set of vortical elements and analyze their collective dynamics. These approaches have recently been generalized to analyze high-dimensional turbulent flows, for which network computations can become prohibitively expensive. In this work, we propose efficient methods to approximate network...

We develop an unsupervised machine learning algorithm for the automated discovery and identification of traveling waves in spatio-temporal systems governed by partial differential equations (PDEs). Our method uses sparse regression and subspace clustering to robustly identify translational invariances that can be leveraged to build improved reduced order models (ROMs). Invariances, whether translational or rotational, are well known to compromise the ability of ROMs to produce accurate and/or lo...

#1Samuel H. Rudy (UW: University of Washington)H-Index: 4

#2J. Nathan Kutz (UW: University of Washington)H-Index: 38

Last.Steven L. Brunton (UW: University of Washington)H-Index: 27

view all 3 authors...

Abstract A critical challenge in the data-driven modeling of dynamical systems is producing methods robust to measurement error, particularly when data is limited. Many leading methods either rely on denoising prior to learning or on access to large volumes of data to average over the effect of noise. We propose a novel paradigm for data-driven modeling that simultaneously learns the dynamics and estimates the measurement noise at each observation. By constraining our learning algorithm, our met...

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