Same here, maths isn't my strong point. Biology based subjects are though as my thought process lends itself more naturally to theory and experimentation, rather than logical based.
I never fully got to grips with Fourier & LaPlace transforms - but that's more because of the way we were taught them which was in a pure mathematical sense, with no concept of real-world application being passed on to us as learners.
I loved Fourier & Laplace transformations. They are some of the more difficult things I've had to do. I can't name anything specific (desperately need to revise) but Thermofluids comes with some odious equations.
I think it's fair to say that given mathematical lexicon, the more difficult the mathematics is, the more incomprehensible (linguistically) it is and the more ridiculous it sounds!
It also very much depends on your area; maths outside your area always seems more difficult on the surface. I do numerical work (applied mathematics) involving things like discretisation and transformation techniques for PDEs and finite-volume method numerical approximations etc. which seems fairly second-nature to me, but seems difficult to other people in my research group. They, however, do excellent work in deconvolution algorithms and Bayesian analysis for NMR spectroscopic data which seems very difficult to me but is second-nature to them; it's simply a matter that I am more used to my own branch of maths than theirs. Probably all seems like gobbledegook to people in other research groups in our department
I used to be able to derive the stick-fixed pitching-moment equation from first-principles. Long time ago, mind. I was another that struggled with Laplace and Fourier Transforms, but I did get on OK with stuff like re-ordering multiple integrals and second-order partial differential equations. Wasn't a fan of fluid dynamics though. The hardest thing I still know is that 2+2=5 for very small values of 5.
i would say my entire signal theory module for my AI and cybernetics degree, but i dont technically know that. its a lot of laplace, fourier and Z transforms, along with some other complex maths for signal sampling and filter design
i guess the hardest maths thing i can do is manually run through the entire algorithm that a multi layer neural network (multi layer perceptron) uses for learning
Representation Theory, but then again I'm more into the applied side and that was horribly pure. Matched Asymptotic Expansions can be quite tricky as well.
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