Data Driven Modal Decompositions: Analysis and Enhancements
نویسندگان
چکیده
منابع مشابه
Parallel QR algorithm for data-driven decompositions
Many fluid flows of engineering applications, although very complex in appearance, can be approximated by lower-order models governed by a few modes, able to capture the dominant behavior (dynamics) of the system. Recently, different techniques have been developed, designed to extract the most dominant coherent structures from the flow. Some of the more general techniques are based on data-driv...
متن کاملEmpirical Mode Decompositions as Data-Driven Wavelet-like Expansions
During the last decade, wavelet-based techniques (and variations) have proved remarkably effective for representing and analyzing various stochastic processes, and especially those with scaling properties [1]. Amongst a number of reasons for this success stands first the adequacy between the multiscale nature of such processes and the built-in multiscale structure of wavelet decompositions, as ...
متن کاملIdentification of BKCa channel openers by molecular field alignment and patent data-driven analysis
In this work, we present the first comprehensive molecular field analysis of patent structures on how the chemical structure of drugs impacts the biological binding. This task was formulated as searching for drug structures to reveal shared effects of substitutions across a common scaffold and the chemical features that may be responsible. We used the SureChEMBL patent database, which prov...
متن کاملOutput-only Modal Analysis of a Beam Via Frequency Domain Decomposition Method Using Noisy Data
The output data from a structure is the building block for output-only modal analysis. The structure response in the output data, however, is usually contaminated with noise. Naturally, the success of output-only methods in determining the modal parameters of a structure depends on noise level. In this paper, the possibility and accuracy of identifying the modal parameters of a simply supported...
متن کاملCUR matrix decompositions for improved data analysis.
Principal components analysis and, more generally, the Singular Value Decomposition are fundamental data analysis tools that express a data matrix in terms of a sequence of orthogonal or uncorrelated vectors of decreasing importance. Unfortunately, being linear combinations of up to all the data points, these vectors are notoriously difficult to interpret in terms of the data and processes gene...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: SIAM Journal on Scientific Computing
سال: 2018
ISSN: 1064-8275,1095-7197
DOI: 10.1137/17m1144155