Data-Driven Modeling & Scientific Computation

This book explores the integration of scientific computing methods with modern data analysis techniques, utilizing MATLAB to address complex systems and big data challenges across various scientific disciplines.

Data-Driven Modeling & Scientific Computation explores the intersection of scientific computing and modern data analysis techniques. The book focuses on integrating methods such as compressive sensing and machine learning with the dynamics of complex systems and big data. Throughout the text, MATLAB is utilized to demonstrate various mathematical solution strategies, making the content accessible and practical for readers.

The rapid growth of data collection across scientific disciplines has created a pressing need for advanced mathematical techniques and computational algorithms that can effectively synthesize and interpret vast data sets. This book aims to bridge the gap between traditional scientific computing methods and contemporary data analysis, presenting a cohesive framework that incorporates key concepts from statistics, time-frequency analysis, and low-dimensional reductions. By merging these areas, the authors provide valuable insights into the data encountered in diverse scientific fields, particularly those arising from complex dynamical systems.

In addition to theoretical concepts, the book emphasizes practical applications by surveying numerical solution techniques for ordinary and partial differential equations, along with algorithms for data manipulation and analysis. It caters to both undergraduate and graduate students, offering a comprehensive resource that combines computational skills with data analysis. The latter sections of the book are particularly engaging, featuring intuitive examples that illustrate how the integration of these methodologies can yield critical insights into various scientific challenges.