Bad state of engineering R-working on empirical data

In summary, the speaker works as a research assistant in a ceramics laboratory and enjoys modeling and analyzing internal stress of mechanical components. However, they find the use of empirical data and fitting into models to be tedious. They also express frustration with the lack of opportunity to create their own finite element models. The speaker is considering pursuing a degree in applied mathematics for more challenging mathematical research.
  • #1
marellasunny
255
3
I work as a research assistant in a ceramics laboratory,a very reputed university.I model and analyse internal stress of certain mechanical components,its super-fun.But,the bad part is when I have use the empirical data and fit into an model.Take for example the mathematical modelling of phase transformations during quenching,tempering ...etc(i.e transient heat conduction problems).These models involve beautiful math,but my supervisor doesn't encourage us getting into the mathematics.I work on finite element models and I don't even have the opportunity to make one of my own.Its all pre-defined and boring.Why has engineering research become like this?Its worse in the companies from what I heard.

I want to take up research involving hard-core mathematics,where I can use my brain. Should I try and get a degree in applied mathematics for this?I have a degree in automotive engineering.
 
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  • #2
Hey marellasunny.

This just my opinion, but I think you're going to get more messy mathematics in engineering or something like physics or actuarial science than mathematics. I'm not saying you can't find it, but just that IMO it's more probable to find it the real applied areas.

Maybe I'm misreading you so it might be useful to get from you what hard-core mathematics is: personally I think some of the stuff in some of the engineering disciplines would be a lot more full on than some of the stuff in some of the applied mathematics areas.
 

1. What is the "bad state of engineering R-working on empirical data"?

The "bad state of engineering R-working on empirical data" refers to the challenges and issues that arise when using the R programming language for analyzing and working with empirical data in the field of engineering. This can include difficulties with data cleaning and manipulation, inefficient coding practices, and limitations in statistical analysis.

2. What are some common problems encountered when using R for engineering data analysis?

Some common problems when using R for engineering data analysis include difficulty in handling large datasets, lack of user-friendly interfaces, and a steep learning curve for beginners. Additionally, R can be slower compared to other programming languages, which can be a drawback for time-sensitive engineering projects.

3. Is R suitable for engineering data analysis?

Yes, R is suitable for engineering data analysis. It is a powerful and versatile programming language with a wide range of statistical and data analysis packages. However, it may not be the best choice for every engineering project, and it is important to consider the specific needs and requirements of the project before deciding on the best tool for data analysis.

4. Are there any alternatives to using R for engineering data analysis?

Yes, there are alternatives to using R for engineering data analysis. Some popular alternatives include Python, MATLAB, and SAS. Each of these languages has its own strengths and weaknesses, and the choice of which one to use will depend on the specific needs of the project and the researcher's comfort level with the language.

5. How can the "bad state of engineering R-working on empirical data" be addressed?

The "bad state of engineering R-working on empirical data" can be addressed by improving coding practices, utilizing efficient data cleaning and manipulation techniques, and staying updated with the latest packages and tools in R. Additionally, seeking help and advice from experienced R users and attending workshops or training programs can also help in improving the state of R-working on empirical data in engineering.

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