Applied and Computational Mathematics at KTH
The programme consists of foundation courses that are mandatory for all students, and once the individual specialisation track is chosen, there are relevant required courses within that area as well. The programme offers four tracks: Computational Mathematics, Financial Mathematics, Optimisation and Systems Theory, and Mathematics of Data Science.
Regardless of which track students attend, the final term consist of a degree project that may be carried out in an academic or industrial environment in Sweden or abroad. Students are welcome to discuss project ideas with the staff of the Department of Mathematics, but are also encouraged to seek other contacts, in the academic world and in industry, to identify suitable projects. The result of the degree project is provided as a written report and as a presentation at a seminar.
Computational Mathematics track
The field of computer simulations is of great importance for high-tech industry and scientific/engineering research, for example virtual processing, climate studies, fluid dynamics and advanced materials. Thus, computational science and engineering is an enabling technology for scientific discovery and engineering design. It involves mathematical modeling, numerical analysis, computer science, high-performance computing and visualisation. The remarkable development of large scale computing in the last decades has turned computational science and engineering into the “third pillar” of science, complementing theory and experiment.
The Computational Mathematics track is mainly concerned with the mathematical foundations of computational science and engineering. However, in this track we will also discuss issues of high-performance computing. Given the interdisciplinarity, the final curriculum may vary greatly depending on your interests.The Computational Mathematics track contains courses providing knowledge of design, analysis and application of numerical methods for mathematical modeling, usable in computer simulations catering to both research and prototyping.
Financial mathematics track
Financial mathematics is applied mathematics used to analyse and solve problems related to financial markets. Any informed market participant would exploit an opportunity to make a profit without any risk of loss. This fact is the basis of the theory of arbitrage-free pricing of derivative instruments. Arbitrage opportunities exist but are rare. Typically both potential losses and gains need to be considered. Hedging and diversification aim at reducing risk. Speculative actions on financial markets aim at making profits. Market participants have different views of the future market prices and combine their views with current market prices to take actions that aim at managing risk while creating opportunities for profits. Portfolio theory and quantitative risk management present theory and methods that form the theoretical basis of market participants’ decision making.
Financial mathematics has received lots of attention from academics and practitioners over recent decades and the level of mathematical sophistication has risen substantially. However, a mathematical model is at best a simplification of the real world phenomenon that is being modeled, and mathematical sophistication can never replace common sense and knowledge of the limitations of mathematical modelling.
Optimisation and Systems Theory track
Optimisation and Systems Theory is a discipline in applied mathematics primarily devoted to methods of optimisation, including mathematical programming and optimal control, and systems theoretic aspects of control and signal processing. The discipline is also closely related to mathematical economics and applied problems in operations research, systems engineering and control engineering. The master’s education in Optimisation and Systems Theory provides knowledge and competence to handle various optimisation problems, both linear and nonlinear, to build up and analyse mathematical models for various engineering systems, and to design optimal algorithms, feedback control, and filters and estimators for such systems.
Optimisation and Systems Theory has wide applications in both industry and research. Examples of applications include aerospace industry, engineering industry, radiation therapy, robotics, telecommunications, and vehicles. Furthermore, many new areas in biology, medicine, energy and environment, and information and communications technology require an understanding of both optimisation and system integration.
Mathematics of Data Science track
Statistics is the science of learning from data. Classical statistics is trying to understand data by determining a plausible model for data, and testing whether the data fits the model. Modern learning is concerned with computational statistics and automated methods for extracting information from data. The technological progress and the increased availability of information contributes to the emergence of massive and complex data sets. A variety of scientific fields are contributing to the analysis of such data at the interface of mathematics, statistics, optimization and computational methods for learning. Optimal decision making under uncertainty based in such circumstances require modelling and discovering relevant features in data, optimization of decision policies and model parameters, dimension reduction and large scale computations. Data science based on applied mathematics has the potential for transformative impact on natural sciences, business and social sciences.
This is a two year programme (120 ECTS credits) given in English. Graduates are awarded the degree of Master of Science. The programme is given mainly at KTH Campus in Stockholm by the School of Engineering Sciences (at KTH).
Specific requirements for the master’s programme in Applied and Computational Mathematics
A bachelor’s degree corresponding to 180 ECTS credits, or equivalent, with at least 45 ECTS credits in Mathematics. The students are required to have documented knowledge corresponding to basic university courses in analysis in one and several variables, linear algebra, numerical analysis, ordinary and partial differential equations and integral transforms, mathematical statistics, and basics of programming in a higher programming language.
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