Physicists working in experimental and theoretical research, as well as in applied fields and industry require the ability to simulate physical processes based on the fundamental laws of physics. Physicists also requires handling increasingly large datasets from measurements and simulations, and the ability to extract relevant metrics and conclusions. This module will teach advanced computational methods of relevance in applied physics. The module has a strong practical computing laboratory component to maximize practical skills development.

The module is suitable for physicists or students in other scientific disciplines who have already attended introductory modules in programming and computing. The module will introduce students to relevant computational methods for simulations and data processing that allows solving problems in physics and engineering. The module includes practical examples and aims at giving hands on skills. The practical examples will be made with Matlab or equivalent. Content: data statistics and plots (variance, mean, histograms, etc); numerical differentiation, integration and other basic operations, including logic statements and coupled equations; Fourier transformation (signal processing, convolution thm, signal analysis, lock-in, phase problem and input-output algorithm) (with practical examples e.g. in image processing); Fitting and optimization (least square fit, particle swarm optimization, error function) (with practical examples e.g. XPS data analysis); machine learning (ANN, data reduction, encoder-decoder, classification/regression); finite element analysis (with practical examples e.g. photonics).

On successful completion of this module, students will be able to: - Identify numerical techniques suitable to solve problems in the physical sciences. - Implement these numerical techniques and simulate physical systems. - Manipulate data for analysis, including large datasets. - Create scientifically appropriate visualizations of data.

On successful completion of this module, students will be able to: - Recognize the limitations and benefits of numerical approaches in the physical sciences. - Value the methodical documentation of code, experimental work, and synthesis of conclusions from data/simulations.

On successful completion of this module, students will be able to: N/A

Students will learn through lecture and weekly project work in the computer laboratory. - Knowledgeable: Syntax of language; application of numerical algorithms; Use of software for data processing and physical simulations. - Creative and proactive: Solving physical problems. - Articulate: Report writing and code documentation.