Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

By Nathan Kutz. 2013

219 citations recorded by the source corpus.

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Citations in the corpus

Modal Analysis of Fluid Flows: An Overview (2017, methodology): “While the method of snapshot is preferred for handling large data sets, the SVD based technique to determine the POD modes is known to be more robust against roundoff errors [3].”
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence (2016, background): “Compressed sensing and machine learning have already been combined to achieve sparse decision making [278, 168, 46, 39, 215], which may dramatically reduce the latency in a control decision.”
A Physics-Constrained Data-Driven Approach Based on Locally Convex Reconstruction for Noisy Database (2019, background): “multaneously to enhance traditional scientific computing and engineering design procedures [1,2] has attracted increasingly attentions. This general approach is usually termed as data-driven modeling [3] or data-driven engineering science. Data-driven modeling has a close connection with the various”
Cluster-based reduced-order modelling of a mixing layer (2013, methodology): “The POD model serves – pars pro toto – as an example for many other data-driven models (Hyvärinen 2012; Kutz 2013).”
Data-Driven Sparse Sensor Placement for Reconstruction: Demonstrating the Benefits of Exploiting Known Patterns (2017, methodology): “[16] J.”
SINDy-PI: a robust algorithm for parallel implicit sparse identification of nonlinear dynamics (2020, methodology): “To generate the simplified BZ reaction data, we use a spectral method [74, 75] with time horizon T = 1”
Compressive Sensing and Low-Rank Libraries for Classification of Bifurcation Regimes in Nonlinear Dynamical Systems (2013, background): “The application of machine learning and compressive sensing to dynamical systems is synergistic in that underlying low-rank structures facilitate sparse measurements [31].”