LFU:online

Prerequisites for registration: none

Learning Outcome: - The students know the most important application possibilities of scientific computing as well as its potential to provide interdisciplinary efficient answers to current questions in research, economy and society. They are aware of gender issues and gender relations in the natural and technical sciences. - Students understand aspects of conventional computer architecture particularly relevant to scientific computing, as well as useful techniques for software development and management, which also guide forms of collaborative work. They are able to implement simple numerical algorithms - basic components of more complex simulations - and to analyse their properties. - They can explain and apply the scientific principles and basic methods of mathematical modelling as a basis for simulation. They are able to analyse simple existing models, select and apply suitable ones from established procedures for their evaluation, and interpret the resulting data in a professional manner.

Prerequisites for registration: none

Learning Outcome: - After completion of the module, students will be able to use common mathematical models of real-world processes and evaluate them efficiently on conventional computer systems. - They have a detailed understanding of performance-relevant aspects of conventional computer architectures as well as appropriate programming skills to efficiently implement numerical methods on them and analyse their properties. - They will be able to correctly discretise the descriptive equations and to identify suitable numerical methods for further evaluation based on the current state of research and adapt them to problems as well as to programming requirements. - Based on this, students develop the competence to improve relevant aspects of numerical methodology and concrete implementation in a reciprocal and evaluation-based way in order to generate and evaluate practical solutions for new numerical problems.

Prerequisites for registration: none

Learning Outcome: - Students will have a detailed understanding of the computer architectures relevant to high-performance computing: the use of massively networked conventional computer systems, the access to perform certain computational operations of specialised accelerators, and the hybrid use of both concepts. - They can use methods to adapt conventional numerical algorithms to high-performance computing architectures as well as numerical methods designed for specialised architectures. You can assess the suitability of specialized HPC architectures for the implementation of given processes and have the programming competence to implement and evaluate efficient solvers of numerical problems.

Prerequisites for registration: none

Learning Outcome: - Students will be familiar with representative examples of applications of scientific computing in various disciplines as well as the subject-specific software used to process them and will be able to discuss both from the perspectives of the various disciplines. - They have the competence - independently and with original ambition - to elicit meaningful models for selected problems from the practice of scientific computing, to construct suitable simulations and to implement, execute and evaluate them efficiently on suitably selected hardware, and to critically evaluate the results obtained. - They have the skills to visualise simulation data in a professional and descriptive manner and to manage it according to the rules of good scientific practice. They are able to communicate, explain, and discuss findings obtained from simulations, useful visualisations, and prerequisites and limitations of the statements made.