ENGSCI 721 : Data-centric Engineering for Physical Systems

Engineering

2025 Semester Two (1255) (15 POINTS)

Course Prescription

Mathematical modelling of complex physical systems, including model development, parameterisation and evaluation, illustrated using examples from current research and industry. Inverse problems and uncertainty quantification for physical models in engineering and science, including principles of uncertainty propagation for linear and nonlinear physical models given real-world data, and connections to physics-informed machine learning.

Course Overview

This course will cover parameterisation, calibration, and evaluation for engineering models of complex physical systems. This will include: forward and inverse problems; well-posed and ill-posed estimation problems; principles of uncertainty propagation for linear and nonlinear physical models given real-world data; Bayesian and Frequentist uncertainty for parameter estimation and prediction; optimisation and sampling methods; physics-informed machine learning. The focus will be on conceptual understanding and implementation of key ideas using example problems.

Course Requirements

Prerequisite: 15 points from COMPSCI 101, ENGGEN 131, MATHS 162, 199; and either 15 points from ENGSCI 311, 313, 314, or MATHS 260 and either STATS 210 or 225

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking

Learning Outcomes

By the end of this course, students will be able to:
  1. Develop an understanding of the different approaches to relating models and data and their domains of applicability. Be able to synthesise different approaches to combine their strengths, e.g., physics informed machine learning. (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)
  2. Understand the concepts of well-posed and ill-posed parameter estimation for physical problems, and the principles of uncertainty quantification for linear and nonlinear physical models given real-world data. (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)
  3. Use uncertainty quantification methods to update and propagate uncertainties in parameter estimates and predictions for physical models given real-world data. (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)

Assessments

Assessment Type Percentage Classification
Tests 40% Individual Coursework
Assignments 30% Individual Coursework
Final Exam 30% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3
Tests
Assignments
Final Exam

A passing mark is 50% or higher, according to University policy.

Students must sit the exam to pass the course. Otherwise, a DNS (did not sit) result will be returned.

Assignments are penalised at 4% of the total mark for each hour. 

There are no late submissions for Quizzes.


Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect 36 hours of lectures and tutorials, and approximately 114 hours throughout the semester reading, thinking about the content and working on assignments and/or test preparation.

Delivery Mode

Campus Experience

Lectures will be available as recordings.

Attendance on campus is required for the test.

The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Health & Safety

Students must ensure they are familiar with their Health and Safety responsibilities, as described in the university's Health and Safety policy.

Student Feedback

At the end of every semester students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback and respond with summaries and actions.

Your feedback helps teachers to improve the course and its delivery for future students.

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

- Based on the 2024 SET results the course will be run as in 2024

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework, tests and examinations as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. A student's assessed work may be reviewed against electronic source material using computerised detection mechanisms. Upon reasonable request, students may be required to provide an electronic version of their work for computerised review.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website http://disability.auckland.ac.nz

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.

Disclaimer

Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course you may be asked to submit your coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 27/11/2024 09:26 p.m.