Modeling and Computational Engineering MOD510
This course introduces numerical methods and modeling techniques used to solve practical problems. The course provides insights and skills in computational thinking and programming techniques
You will learn the most common numerical methods used to solve complex physical, biological, financial or geological phenomena. Examples of methods are numerically derivation, numerical integration, Monte Carlo and boot strapping methods, inverse methods, numerical solution of common differential equations, simulated annealing, lattice Boltzmann models, random walk models, box (compartment) models.
The primary programming language is Python. Through assignments, you will learn how to set up mathematical models of a phenomenon, develop algorithms, implement them, and investigate the strength and limitations of the solution method and the mathematical model. You will learn how to code efficiently in Python, both by using functions and classes. The projects focus on modeling realistic systems, and to compare with measured data to learn more about the systems. The goal of the projects is to reproduce state of the art scientific results.
Course description for study year 2021-2022. Please note that changes may occur.