Computational Engineering - learning outcomes
A candidate with a Master degree in Computational Engineering at UiS will have the following overall learning outcomes defined in terms of knowledge, skills and general competence:
K1: Has advanced knowledge in the field of uncertainty quantification and modeling for decision support. This means that the candidate has the ability to develop mathematical models that account for uncertainties contained in incomplete data and information and provide the basis for improved understanding and interpretation of data as well as for decision support.
K2: Advanced knowledge of effective methods for designing, developing and testing models.
K3: Advanced knowledge in the use of algorithms and computational thinking to solve discrete and continuous problems.
K4: Understand the limitations introduced by representing a complex system with a model.
K5: Understand the constraints associated with the chosen solution method, including approximation errors and constraints linked to the selection of specific algorithms or numerical methods.
K6: Understand the importance of quantifying relevant and material uncertainties to generate insight and informed decisions.
K7: Deep understanding of the significance and consequences imbedded in the well-known quote: “All models are wrong, but some models are useful” (George Box, 1978).
S1: Analyze and act critically to different sources of information and apply them to structure and formulate professional and scientific reasoning according to modeling, uncertainty quantification, simulation, optimization and decision support.
S2: Detailed knowledge and experience of programming in at least one high level programming language.
S3: Determine model parameters using data and expert knowledge.
S4: Be able to find the right balance between a model's usefulness (how credible is the understanding generated by the model) and manageability (any analysis must be completed within given time and resource constraints).
S5: Develop custom modeling programmes for specific decision- or optimization situations.
S6: Model systems and develop new instruments and applications for gathering relevant data, analysis and management in accordance with established engineering principles.
S7: Evaluate instruments and applications to quantify the value of information and to optimize the data gathering, analysis and management.
S8: Perform sensitivity analysis of model parameters to generate additional insights and understanding.
G1: Develop hypotheses and suggest systematic ways to test these using mathematical models.
G2: Communicate in a professional way about scientific problems, decisions, results of data, uncertainty, and modeling analysis - both to specialists and to the general public.
G3: Utilize the generic nature that lies in the use of mathematical formulations to actively seek to transfer knowledge between different applications.
G4: Utilize the mathematical formulation to gain insight into the core of the problem that is uncover the most basic mechanisms that govern the process being studied.
G5: Insight into “The Art and Science of Mathematical Modeling”.