MATH4337: Uncertainty Quantification IV

• Statistical Inference (MATH2711)

• None

• None

• To be able to design computer experiments and develop methods and procedures for the statistical analysis of modern large-scale applications in order to quantify uncertainties and perform predictions.

• Introduction to Complex models of physical systems
• Emulation
• Linking the Model to Reality
• Inverse problems
• Design of computer experiments
• Sensitivity analysis

Subject-specific Knowledge:

• On the successful completion of the module, students will be able to:
• Identify and explain the nature and sources of uncertainty in large-scale applications.
• Develop statistical models for the emulation of expensive computer models of physical systems.
• Explain and justify the principles of experimental design for computer models, and apply computer model design to appropriate applications.
• Explain and combine the key components of an Uncertainty Quantification for the analysis of large-scale applications.
• Implement Uncertainty Quantification methodology in a computer programming language and drawn meaningful insight and conclusions from the output.

Subject-specific Skills:

• Students will develop advanced statistical skills and relevant mathematical skills in modelling and computation.

Key Skills:

• Students will develop advanced skills in problem solving, critical and analytical thinking, and communicating scientific results by written reports.

• Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
• Computer practicals consolidate the studied material, explore theoretical ideas in practice, enhance practical understanding, and develop practical data analysis skills.
• Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
• Formative assessments provide feedback to guide students in the correct development of their knowledge and skills in preparation for the summative assessment.
• The written project report assesses the ability to implement the concepts introduced in the module using statistical software, to apply them in the analysis of a realistic problem, and to report scientific outputs in a clear and structured way.
• The end-of-year examination assesses the knowledge acquired and the ability to solve predictable and unpredictable problems.

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