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. The enormous data sets now routinely encountered
in the sciences provide an incentive to develop mathematical
techniques and computational algorithms that help synthesize,
interpret and give meaning to the data in the context of its
scientific setting. A specific aim of this book is to integrate
standard scientific computing methods with data analysis. By doing
so, it brings together, in a self-consistent fashion, the key ideas
from: * statistics, * time-frequency analysis, and *
low-dimensional reductions The blend of these ideas provides
meaningful insight into the data sets one is faced with in every
scientific subject today, including those generated from complex
dynamical systems. 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 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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