Uncertainty Quantification Courses
Uncertainty Quantification courses offer an opportunity to learn the discipline of modeling, analyzing and understanding uncertainties in physical systems. This encompasses a wide range of topics, from stochastic analysis of data and systems to probabilistic methods of treating uncertainty and randomness in parameters, variables and outputs. Generally, courses cover theoretical and practical aspects of modeling systems and data, evaluating uncertainties and their impact, and handling the resulting data with algorithms and heuristics. The courses also give a chance to apply the theory of Uncertainty Quantification to complex problems in science, engineering and finance. At a fundamental level, students learn to use mathematical models and computer simulations to analyze data and estimate parameters, evaluate models and interpret the uncertainties they contain. Theoretical topics discussed include stochastic processes, probability theory, and analysis of variance. Practical topics include Monte Carlo simulation techniques, data modeling techniques, and uncertainty propagation methods. The kind of students expected to enroll in an Uncertainty Quantification course include mathematicians, computer scientists, physicists, and engineers. Generally, a background in mathematics, probability, statistics and computer programming is required. Additionally, working knowledge of numerical analysis, uncertainty propagation methods and system dynamics is beneficial. While widely used in all fields of science, engineering and finance, uncertainty quantification courses are rarely available in the United States. However, the research-oriented ones are becoming more common, and more universities are offering to teach them. Students who are serious about learning Uncertainty Quantification should look for these courses, as they provide an invaluable tool for addressing complex problems.
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