Expert knowledge (((on computational engineering)))

Hey, remember this handy Venn diagram?

Cool, right? Now, it’s almost time to learn a bit more about one of those fancy terms on the diagram! But first, a light preamble…

A few weeks ago, in an ENGSCI 263 lecture, we were introduced to the term ‘expert knowledge’. In the context of ENGSCI 263, expert knowledge might be knowing how a physical system has been modeled in the past or what a sensible range of values for a parameter might look like. In a broader sense, this term encompasses the idea of how experience and insight can benefit our decision making; how fitting this is for a first-year engineer trying to choose their spec.

My goal as a part II blogger is to give you the best insight I can into engineering science, but I am just a second year who lacks experience in the field (I have also lacked time in the last few weeks to write a decent blog post). Better yet, I myself am still not completely aware of the possibilities and opportunities held by Engineering Science. So, in a move of sheer genius I contacted some members of the Department of Engineering Science and asked them a few questions in hope that I could tap into their expert knowledge.

The responses I received come from Doctor David Dempsey and Associate Professor Piaras Kelly. These two are involved more in the computational engineering area of engineering science, which is an area I am becoming more interested in the more I learn about it! I asked three simple questions and their responses are presented below verbatim. Keep in mind, this in not ‘official advice’, it is the opinion of the individuals I contacted.


Q1: What areas of the engineering science realm (i.e. computational engineering, data analysis, operations research) are you involved in and what do you enjoy about these areas of engineering science?


I am mainly involved in computational engineering. Day to day, I am running physics-driven models of earthquakes, volcanoes, and geothermal systems. More recently, I am looking at how we can improve these physics models with machine learning, e.g., neural networks to accelerate run time of earthquake models, time series feature extraction for volcano forecasting.


I am involved in solid mechanics and computational mechanics, and the interface between these and other fields of engineering.  This in theory would include any and all applications that involve something “solid”, but of course I can only focus on a number of distinct areas of interest; these include the application of these in geomechanics (e.g. ice mechanics, soil mechanics, earthquake engineering), the manufacture of “new” materials (e.g. composite materials for aerospace applications), and the performance of new structural components (e.g. knee implants). I enjoy many different aspects to these problems, for example the mathematics involved, which includes linear algebra, calculus and differential geometry; the physics involved, which includes the mechanics of materials and thermomechanics; and also the numerical and computational algorithms required to solve the various equations governing the problem at hand. Also, I of course enjoy the research-based and more applied/practical questions which can be answered using these theories, and the process of moving through the complete solution chain, from fundamental theory to the attainment of a useful outcome or recommendation.


Q2: Are there any second/third/fourth year engineering science courses that interest you personally and why?


I really like all the courses I teach in. Love the programming in ENGSCI233, and the freedom it gives students once they get halfway decent at it. ENGSCI343 is a fabulous intro to fluids and solids, as they underpin so much of the geophysical phenomena I study (and because it gives me a vehicle to do the E=mc^2 proof). And ENGSCI263 is probably my overall favourite, mainly because it’s a chance to show you guys how everything fits together, and why Engineering Science is useful.


I especially like ENGSCI 344, a third-year elective that looks at the computational modelling of practical scenarios. It has got some great “engineering science”, in the sense of using mathematics/physics and computing to solve real world problems. There is minimal theory involved and the paper is more focused on how to use a computer to answer some real-world problem. Many different aspects of a such problems are addressed, for example what exactly should be considered in a computational model, what should be safely “ignored”, what is it that the computer is doing when you hit “solve”, what are the automated aspects of the computer solver which you can trust and which should you be sceptical about, what is the best computational solution strategy to use in any given scenario, and how do you know whether the computer model output is correct, or accurate, or even that the computer has solved the problem you thought it was solving.


Q3: Asides from reading course descriptions, do you have any recommendations for how an engineering science student could decide to tailor their degree?


Talk to people in the year above you who have taken particular electives. You’ll often get a better sense of whether the course is suited to you than just reading the description. Talk to Kevin Jia about the Math and Stats courses, he seems to know a lot. Last thought on tailoring an ENGSCI degree. The world is increasingly multidisciplinary. Some diversification in your degree wouldn’t hurt. Consider outside the box electives – economics, politics, philosophy, or a second language.


Some students have a focused approach and a clear picture of where exactly they want to end up (career or research wise) and choose papers accordingly. However, many students pick and mix courses and papers as they go along, to match their interests at the time. It is often best to first try papers and see what you think about them before making any cast-iron decisions about what you would like to do in the future. Your interests will develop and change as new information and knowledge is gained, so it is probably best to choose papers early on which keep your options open. Also, there are many different papers and routes a student could take. I would advise students to speak with staff at the Department to find out more about certain papers, and especially to speak with the Departmental Undergraduate Course Adviser, who has data and experience on what students have done in previous years, and how those decisions worked out.

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