Investigate the flexibility and power of project-oriented computational
analysis. Practice using this technique to resolve complicated
problems in a range of fields including the physical and
engineering sciences, finance and economics, medical, social and
biological sciences. Enhance communication of information by creating
visual representations of scientific data.
This course is a survey of numerical solution techniques for ordinary and partial differential equations. Emphasis will be on the application of numerical schemes to practical problems in the engineering and physical sciences. Apply advanced MATLAB routines and toolboxes to solve problems. Review and practice graphical techniques for information presentation and learn to create visual illustrations of scientific results
To be successful in the course, a strong background in linear algebra
is required. Familiarity with methods of ordinary differential
equations and basic programming structure is also required. With this
background, students should be able to develop the codes necessary for
the homework in the course.
Given the computational nature of the course, access to MATLAB (www.mathworks.com) or Octave (www.gnu.org/software/octave) is essential. Octave is a free (or by donation) alternative to MATLAB that can also be downloaded and installed via the web. Either software should suffice for all the needs of the course, but MATLAB is the strongly recommended alternative.
Kutz, N. (2013). Data-driven modeling scientific computation. New York, NY: Oxford University Press.
Data-Driven Modeling and Scientific Computation is a survey of practical numerical solution techniques for ordinary and partial differential equations as well as algorithms for data manipulation and analysis. Emphasis is on the implementation of numerical schemes to practical problems in the engineering, biological and physical sciences.
An accessible introductory-to-advanced text, this book fully integrates MATLAB and its versatile and high-level programming functionality, while bringing together computational and data skills for both undergraduate and graduate students in scientific computing.