# ENGSCI 721 : Data-centric Engineering for Physical Systems

## Engineering

### 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

ENGSCI 721 Data-centric Engineering Physics

(The University Calendar officially lists this as “Advanced Numerical Methods”; this course has been updated/changed)

This course will cover mathematical modelling of complex physical systems, focusing on model development, parameterisation and model evaluation. The first part of the course will focus on elements of modelling, illustrated using wave-type processes, diffusion, porous media flow, and other examples with applications in filtration, manufacturing and reservoir engineering. The latter two-thirds of the course will cover inverse problems and uncertainty quantification for physical models in engineering and science. 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; connections to physics-informed machine learning. The focus will be on conceptual understanding and implementation of key ideas using example problems.

### Course Requirements

Prerequisite: Departmental approval

### Capabilities Developed in this Course

 Capability 1: Disciplinary Knowledge and Practice Capability 2: Critical Thinking Capability 3: Solution Seeking

### Learning Outcomes

By the end of this course, students will be able to:
1. Construct and critically evaluate models of physical systems by applying the appropriate physical laws and assumptions. Obtain and interpret solutions . (Capability 1, 2 and 3)
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 1, 2 and 3)
3. Use uncertainty quantification methods to update and propagate uncertainties in parameter estimates and predictions for physical models given real-world data. (Capability 1, 2, and 3).

### Assessments

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

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

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

As per Health and Safety requirements for lecture-based courses

### 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.

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The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework 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. This requirement also applies to sources on the internet. A student's assessed work may be reviewed against online source material using computerised detection mechanisms.

### 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

### 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 .

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 course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, 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 .

### 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 students may be asked to submit 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. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 14/11/2022 09:39 p.m.