Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

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J. Nathan Kutz (Professor of Applied Mathematics and Electrical and Computer Engineering, Professor of Applied Mathematics and Electrical and Computer Engineering, University of Washington)

Data-Driven Modeling & Scientific Computation

Name: Data-Driven Modeling & Scientific Computation
Price: 334.95 AUD
Availability: PreOrder
ISBN: 9780198929093

Methods for Complex Systems & Big Data

J. Nathan Kutz (Professor of Applied Mathematics and Electrical and Computer Engineering, Professor of Applied Mathematics and Electrical and Computer Engineering, University of Washington)

$334.95

Hardback

Forthcoming
Pre-Order now

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English

Oxford University Press
21 May 2026

Data analysis: general; Complex analysis, complex variables; Numerical analysis; Algorithms & data structures

Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data is an accessible introductory-to-advanced textbook focusing on integrating scientific computing methods and algorithms with modern data analysis techniques, including basic applications of machine learning in the sciences and engineering. Its overarching goal is to develop techniques that allow for the integration of the dynamics of complex systems and big data. This comprehensive textbook provides a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation, data-driven modelling, and machine learning. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological, and physical sciences. The high-level programming language python is used throughout the book to implement and develop mathematical solution strategies. One specific aim of the book is to integrate standard scientific computing methods with the burgeoning field of data analysis, machine learning and Artificial Intelligence (AI). This area of research is expanding at an incredible pace in the sciences due to the proliferation of data collection in almost every field of science. The enormous data sets routinely encountered in the sciences now certainly give a big 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. This brings together, in a self-consistent fashion, the key ideas from (i) statistics, (ii) time-frequency analysis and (iii) low-dimensional reductions in order to provide meaningful insight into the data sets one is faced with in any scientific field today, including those generated from complex dynamic systems. This is a tremendously exciting area and much of this part of the book is driven by intuitive examples of how the three areas (i)-(iii) can be used in combination to give critical insight into the fundamental workings of various problems.

By: J. Nathan Kutz (Professor of Applied Mathematics and Electrical and Computer Engineering Professor of Applied Mathematics and Electrical and Computer Engineering University of Washington)
Imprint: Oxford University Press
Country of Publication: United Kingdom
Dimensions: Height: 246mm, Width: 189mm,
ISBN: 9780198929093
ISBN 10: 0198929099
Pages: 576
Publication Date: 21 May 2026
Audience: College/higher education , Further / Higher Education , A / AS level
Format: Hardback
Publisher's Status: Forthcoming

J. Nathan Kutz is the Boeing Professor of AI and Data-Driven Modeling at the University of Washington. He is with the Department of Applied Mathematics and Electrical and Computer Engineering and is also Director of the AI Institute in Dynamic Systems at the University of Washington. He received the BS degree in physics and mathematics from the University of Washington in 1990 and the PhD in applied mathematics from Northwestern University in 1994. He was a postdoc in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control.

Reviews for Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data

Review from previous edition The book allows methods for dealing with large data to be explained in a logical process suitable for both undergraduate and post-graduate students ... With sport performance analysis evolving into deal with big data, the book forms a key bridge between mathematics and sport science * John Francis, University of Worcester *