Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data
Autor J. Nathan Kutzen Limba Engleză Paperback – 17 iun 2026
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Specificații
ISBN-13: 9780198929086
ISBN-10: 0198929080
Pagini: 608
Ilustrații: 240 b/w illustrations
Dimensiuni: 190 x 245 x 29 mm
Greutate: 1.3 kg
Editura: OUP OXFORD
Colecția OUP Oxford
Locul publicării:Oxford, United Kingdom
ISBN-10: 0198929080
Pagini: 608
Ilustrații: 240 b/w illustrations
Dimensiuni: 190 x 245 x 29 mm
Greutate: 1.3 kg
Editura: OUP OXFORD
Colecția OUP Oxford
Locul publicării:Oxford, United Kingdom
Recenzii
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
Notă biografică
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.
Cuprins
- Prolegomenon to modern computing
- Part 1. Basic computations and visualization
- 1: Python introduction
- 2: Linear systems
- 3: Numerical differentiation and integration
- 4: Curve fitting
- 5: Basic optimization
- 6: Advanced curve fitting and machine learning
- 7: Visualization
- Part 2. Differential and partial differential equations
- 8: Initial and boundary value problems of differential equations
- 9: Finite difference methods
- 10: Time and space stepping schemes: methods of lines
- 11: Spectral methods
- 12: Finite element methods
- Part 3. Computational methods for data analysis
- 13: Statistical methods and their applications
- 14: Time-frequency analysis: Fourier transforms and wavelets
- 15: Matrix decompositions
- 16: Independent component analysis
- 17: Unsupervised machine learning
- 18: Supervised machine learning
- 19: Reinforcement learning
- 20: Spatio-temporal data and dynamics
- 21: Data assimilation methods
- Bibliography
- Index