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Data-driven modeling & scientific computation : methods for complex systems & big data /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Oxford :
Oxford University Press,
2013
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| Edición: | First edition |
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Part I. Basic computations and visualization. MATLAB introduction
- Linear systems
- Curve fitting
- Numerical differentiation and integration
- Basic optimization
- Visualization
- Part II. Differential and partial differential equations. Initial and boundary value problems of differential equations
- Finite difference methods
- Time and space stepping schemes : method of lines
- Spectral methods
- Finite element methods
- Part III. Computational methods for data analysis. Statistical methods and their applications
- Time-frequency analysis : fourier transforms and wavelets
- Image processing and analysis
- Linear algebra and singular value decomposition
- Independent component analysis
- Image recognition : basics of machine learning
- Basics of compressed sensing
- Dimensionality reduction for partial differential equations
- Dynamic mode decomposition
- Data assimilation methods
- Equation-free modeling
- Complex dynamical systems : combining dimensionality reduction, compressive sensing and machine learning
- Part IV. Scientific applications. Applications of differential equations and boundary value problems
- Applications of partial differential equations
- Applications of data analysis.