Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data
Contents:
Finite Difference Methods 9.
Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data
Time and Space Stepping Schemes: Method of Lines Statistical Methods and Their Applications Fourier Transforms and Wavelets Image Processing and Analysis Linear Algebra and Singular Value Decomposition Independent Component Analysis Basics of Compressed Sensing Dimensionality Reduction for Partial Differential Equations Dynamic Mode Decomposition Data Assimilation Methods Applications of Partial Differential Equations Applications of Data Analysis.
Kutz was awarded the B. Professor Kutz is especially interested in a unified approach to applied mathematics that includes modeling, computation and analysis. His area of current interest concerns phenomena in complex systems and data analysis dimensionality reduction, compressive sensing, machine learning , neuroscience neuro-sensory systems, networks of neurons , and the optical sciences laser dynamics and modelocking, solitons, pattern formation in nonlinear optics.
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Prices are subject to change without notice. Pub date Aug Description Index About the author The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. By doing so, it brings together, in a self-consistent fashion, the key ideas from: This is a particularly exciting field and much of the final part of the book is driven by intuitive examples from it, showing how the three areas can be used in combination to give critical insight into the fundamental workings of various problems.
Data-driven modeling & scientific computation : methods for complex systems & big data
Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences. An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.
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Kutz was awarded the B. Professor Kutz is especially interested in a unified approach to applied mathematics that includes modeling, computation and analysis.
The burgeoning field of data analysis is expanding at an incredible pace due to the proliferation of data collection in almost every area of science. Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data. Buy Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data on www.farmersmarketmusic.com ✓ FREE SHIPPING on qualified orders.
His area of current interest concerns phenomena in complex systems and data analysis dimensionality reduction, compressive sensing, machine learning , neuroscience neuro-sensory systems, networks of neurons , and the optical sciences laser dynamics and modelocking, solitons, pattern formation in nonlinear optics. Flowing text, Original pages. Web, Tablet, Phone, eReader.
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Data-Driven Modeling of Complex Systems. Data-driven dynamical systems is a burgeoning field?
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This is a critically important new direction because the governing equations of many problems under consideration by practitioners in various scientific fields are not typically known. Thus, using data alone to help derive, in an optimal sense, the best dynamical system representation of a given application allows for important new insights. The recently developed dynamic mode decomposition DMD is an innovative tool for integrating data with dynamical systems theory.