Data-driven computational methods : parameter and operator estimations /
Harlim, John,
Data-driven computational methods : parameter and operator estimations / John Harlim, the Pennsylvania State University. - xi, 158 pages : illustrations (some color) ; 26 cm
Includes bibliographical references (pages 151-155) and index.
Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.--
9781108472470 1108472478
2018275521
GBB8B9910 bnb
018921686 Uk
Mathematical statistics.
Stochastic analysis.
Computer science.
Computer science.
Stochastic analysis.
QA276.H365 2018
519.5 519.2
Data-driven computational methods : parameter and operator estimations / John Harlim, the Pennsylvania State University. - xi, 158 pages : illustrations (some color) ; 26 cm
Includes bibliographical references (pages 151-155) and index.
Modern scientific computational methods are undergoing a transformative change; big data and statistical learning methods now have the potential to outperform the classical first-principles modeling paradigm. This book bridges this transition, connecting the theory of probability, stochastic processes, functional analysis, numerical analysis, and differential geometry. It describes two classes of computational methods to leverage data for modeling dynamical systems. The first is concerned with data fitting algorithms to estimate parameters in parametric models that are postulated on the basis of physical or dynamical laws. The second is on operator estimation, which uses the data to nonparametrically approximate the operator generated by the transition function of the underlying dynamical systems. This self-contained book is suitable for graduate studies in applied mathematics, statistics, and engineering. Carefully chosen elementary examples with supplementary MATLAB® codes and appendices covering the relevant prerequisite materials are provided, making it suitable for self-study.--
9781108472470 1108472478
2018275521
GBB8B9910 bnb
018921686 Uk
Mathematical statistics.
Stochastic analysis.
Computer science.
Computer science.
Stochastic analysis.
QA276.H365 2018
519.5 519.2