# Numerical Methods in Biomedicine

## Course Description

• ### Course Name

Numerical Methods in Biomedicine

• ### Area of Study

Biomedical Engineering, Biomedical Sciences

• ### Language Level

Taught In English

• ### Course Level Recommendations

Lower

ISA offers course level recommendations in an effort to facilitate the determination of course levels by credential evaluators.We advice each institution to have their own credentials evaluator make the final decision regrading course levels.

### Hours & Credits

• ECTS Credits

6
• Recommended U.S. Semester Credits
3
• Recommended U.S. Quarter Units
4
• ### Overview

Numerical methods in biomedicine (257 - 15543)
Study: Bachelor in Biomedical Engineering
Semester 2/Spring Semester
2nd Year Course/Lower Division

Compentences and Skills that will be Acquired and Learning Results:

- Using NUMERICAL METHODS -NM- to calculate approximate solutions of models of physiological, cellular, and molecular systems.
- Study the stability and accuracy of NM.
- Calculate numerical solution of systems of nonlinear equations.
- Approximate the minimum of a function of several variables.
- Developing, analyzing, and implementing finite difference methods.
- Solving ordinary differential equations and systems by numerical integration methods.
- Using the software environments to discuss the efficiency, pros and cons of different NM.

Description of Contents: Course Description

PROGRAMME

1- PRINCIPLES OF NUMERICAL MATHEMATICS.
Well-Posedness and Condition Number of a Problem
Stability of Numerical Methods.
The Floating-Point Number System.

2- ROOTFINDING OF NONLINEAR EQUATIONS.
Conditioning of a Nonlinear Equation.
The Newton-Raphson Method.
Newton's Methods for Simultaneous Nonlinear Equations.

3- UNCONSTRAINED OPTIMIZATION.
Necessary and Sufficient conditions for Optimality. Convexity.
Optimization Methods.

4- FINITE DIFFERENCE METHODS: INTERPOLATION, DIFFERENTIATION AND INTEGRATION.
Backward, Forward, and Central Differences.
Interpolation and Extrapolation methods.

5- NUMERICAL SOLUTIONS OF ORDINARY DIFFERENTIAL EQUATIONS (ODEs).
ODEs and Lipschitz Condition.
One Step Numerical Methods.
Zero-Stability, Convergence Analysis and Absolute Stability.
Consistency.
Numerical methods for ODEs.
Systems of ODEs.
Stiff Problems.

6- APROXIMATION THEORY.
Fourier Transform.

Learning Activities and Methodology:

One of the purposes of this course is to provide the mathematical foundations of numerical methods, to analyze their basic theoretical properties (stability, accuracy, computational complexity), and demonstrate their performances on examples and counterexamples which outline their pros and cons. The primary aim is to develop algorithmic thinking-emphasizing on long-living computational concepts. Every chapter is supplied with examples, exercises and applications of the discussed theory. The course relies throughout on well tested numerical procedures for which we include codes and test files.

Students should write their own codes by studying and eventually rewriting the codes given by the Teacher in Aula Global. The personal codes should be run, tested and given up in Aula Global in the Computer Room classes.

Throughout the course we emphasize graphic 2D and 3D representations of solutions. Through this visual approach, students will have a chance to experience the meaning, i.e. to understand what a solution means and how it behaves.

Assessment System:

The final grade will come from: 60% final exam + 40% midterm short exams, computational exercises, numerical problems and applications that will be sent by Aula Global in the computer room sessions.

Basic Bibliography:

K. Atkinson. Elementary Numerical Analysis. John Wiley & Sons. 2004
A. Belegundu and T. Chandrupatla. Optimization Concepts and Applications in Engineering. Cambridge University Press, Second Edition. 2011.. 2011
R. L. Burden, J. D. Faires . Numerical Methods. Brooks/Cole, Cengage Learning,. 2003
S. Dunn, A. Constantinides and P. Moghe. Numerical Methods in Biomedical Engineering. Elsevier Academic Press. 2010
Peter Deuflhard and Andreas Hohmann. Numerical Analysis in Modern Scientific Computing. An Introduction. Second Edition.. Springer. 2003
P.E. Frandsen, K. Jonasson, H.B. Nielsen, O. Tingleff. Unconstrained Optimization. IMM, DTU. 1999
A. Quarteroni, R. Sacco and F. Saleri. Numerical Mathematics. Springer. 2007
Lloyd N. Trefethen. Finite Difference and Spectral Methods for Ordinary and Partial Differential Equations. freely available online. 1996