This module comprises two interlinked parts. The Theory, Application and Coding (TAC) part focuses on teaching and applying
the fundamental concepts on modelling, uncertainty and data (MUD), as well as coding skills. In the Q1 Project part students
work in multidisciplinary teams on cases in the context of a smart society, where they will apply the knowledge and skills from
the TAC. In the Q2 project, the students work at the interface areas where the three topics overlap, creating opportunities for
more integrated applications and the ability to focus on fields of interest per programme (when needed) while satisfying the same
set of learning objectives. A gradually increasing complexity and openness of inquiry will be applied.
Study Goals After successfully completing the MUDE a student
General
is able to describe and formulate a research question (or alternatively, design requirements) given a set problem and select the
appropriate methodology and tools
is able to present a fitting work plan to investigate a set of research questions or design requirements
is able to compose a technical document using appropriate academic language and citation with references
is able to work in a collaborative group environment effectively
is able to code according to basic coding standards (e.g., consistency, readability, conciseness, structure, etc.) and collaborate
with their peers via distributed control software (e.g., git)
can perform spatial / temporal / multivariate analysis of data to extract knowledge via physics-based modelling tools, data-
driven approaches, and uncertainty quantification methods
can present and communicate with peers in Civil Engineering and Geosciences, results of analyses using specific modelling,
uncertainty and data approaches with appropriate metrics and visualization techniques
Modeling
can design a modelling framework (from problem conceptualization to governing equation setup) for a physical/engineering
process
can translate the modelling framework into discretized equations and computer code
can mathematically formulate and solve an optimization problem and discuss its properties
can assess optimization and simulation models performance using a set of indicators
Uncertainty
can derive relevant models and complete probability and statistic calculations for faculty-wide applications
can construct relevant models for probabilistic dependence, for example using bivariate distributions or discrete Bayesian
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Networks
use deterministic models with probabilistic inputs to evaluate engineering questions to quantify model output uncertainty (in
time and/or space)
use risk and reliability analyses concepts to describe systems, their characteristics and behaviour in time and space to support
decision making under uncertainty
Data
can identify parameters of interest, describe the functioning of common field/remote sensors used for measuring them, and
explain resolution implications in space and time
is able to apply parameter estimation using observations and perform a quality assessment
can illustrate the procedure of gathering data from different sources, pre-processing it for analysis purposes, and sharing it
according to FAIR principles
