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This course introduces students to robust practices for the quality-assured application of mathematical models in engineering problem solving. Using simple ODE, machine-learning, and generative AI models drawn from Civil Engineering subdisciplines, students will apply a structured approach to model design, testing and utilisation, including key steps of conceptualisation, verification & validation, calibration, prediction, and uncertainty analysis. By the end of this course, students will appreciate the importance of modelling for solving engineering problems, its limitations, and how model insights must be weighed against financial, sociocultural and legal considerations. They will further have an in-depth appreciation of how such practices can be integrated in engineering workplaces, and what risks and opportunities this introduces.
Mathematical and numerical modelling are increasingly present in professional engineering practice. This has been supported by the growing availability of modelling software with easy-to-use interfaces and powerful workflows. Furthermore, recent advances in data science, machine learning and AI have enormous implications for the future of the engineering profession. However, there is a risk that these modern tools could be thoughtlessly used as black boxes without a proper understanding of their correct use and limitations. Moreover, their responsible integration into engineering organisations and processes requires careful consideration.This course is designed to guide you through a principled modelling process, from start to end (problem to solution). Through computer labs and design projects, this course will challenge you to hone new skills of model calibration, prediction, and uncertainty analysis, and become familiar with modern machine learning concepts. Through groupwork, discussion and communication, you will have opportunities to practice complex mental skills, including decision-making, compromise, criticism, and justification. Finally, you will be challenged to reflect how these new tools can best be integrated into engineering workflows.Examples will be drawn from across Civil Engineering subdisciplines, including structural, transport, water, construction management and geotechnical engineering. You will be assigned a project.This course assumes an introductory level understanding of the Python programming language.
1.Synthesise key techniques involved at different stages of physics-driven modelling, including model design, implementation, quality assurance, and application under uncertainty.2. Derive technical insight by performing a physics-driven modelling study for an engineering problem, reflecting critically on the limits of this knowledge in a wider context.3. Evaluate the risks and opportunities of applying generative AI models and workflows to an engineering problem4. Evaluate the strengths and limitations of key concepts of data-driven modelling, including data transformation, statistical analysis, cross-validation, and regression, classification & clustering machine learning algorithms.
ENCN404
Students must attend one activity from each section.
David Dempsey
Alberto Ardid and David Dempsey
All communication with the class will be through lectures, Learn and email.
Internal AssessmentComputer lab tutorials - 10%Physics model report - 15%Generative AI report - 15%Invigilated assessmentMidterm test - 20%Final exam - 40%Total - 100%
The course comprises two sections on physics-driven and data-driven (machine learning) modelling. Each section is delivered as a series of lectures and computer-based labs that deliver their own learning objectives. Here is the approximate time spent on the different course sectionsPhysics-driven models•Design philosophy (2 weeks)•Quality assurance (2 weeks)Large language models (Generative AI) (2 weeks)Data-driven models•From Data Analysis to Data Science (2 weeks)•Machine Learning Algorithms for Prediction (4 weeks)
Domestic fee $1,344.00
International Postgraduate fees
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see Civil and Environmental Engineering .