/sci/ - Science & Math

>>12301172
sine/cosine and sheeeit

>>12301172
Learn signals and control theory.

>>12301172
Just study.

Is building model some pleb tier shit hobby? I use statistics, differentials and vectors to build predictive model. It's pretty fun for me tho.

Control theory, optimization, probability and statistics, linear programming, PDEs, dynamical systems depending on what your model involves. (That's what my uni's applied maths program covers.) I'm sure you can practice modelling problems using only elementary calculus (and less), I can't recommend a good book or resource that I know of though

>>12301172
Geometrical probability.

>>12304694
>>12305829
>>12304550
Thank you so much /sci/.

>>12307151
>>12305829
>>12304694
>>12304550
>>12303055
>>12303024
>>12302971
>>12301172
What are you guys trying to solve with maths? I think this is a potential good thread/general. Model General - /model/

>>12307529
I am sure /sci/ will crucify me because I study discrete optimization primarily. I use linear programming and integer programming models daily for work and school. Most recently for course work was working on vehicle routing problems comparing different integer and constraint programming models.

>>12301172
>building mathematical models
What systems are you modeling?

I'll speak only from modeling natural systems, as that is where most of what my experience is with. Developing mathematical models assumes a concrete understanding of ODEs and PDEs, either one (or both) depending on the nature of the systems you intend to model. If your systems only evolve in relation to time spend most of your time on ODEs. If space or some other variable crops up a lot some time should be spent on PDEs. Dynamical systems is another subject to become well-acquainted with, as this has applications in chemistry to engineering to physics to biology etc. Beyond these subjects some knowledge in statistics and probability theory is necessary, as your models may not always be deterministic but stochastic, and a knowledge of these subjects will help when randomness plays a part in the evolution of your system (which it almost always does). An acquaintance with these subjects will help you build your models, but actually analyzing them is a different story. You'll probably pick up some familiarity along the way studying the aforementioned fields, but just in case some additional familiarity is useful: optimization/control theory (e.g. "what is the optimal trajectory for this system?"), as one anon mentioned, multivariable calc, linear algebra (although this is a prereq for the prereqs imo), a decent amount of programming skills, networks/graph theory (depends on the models), bifurcation theory, sensitivity analysis, blah blah blah

Hope this gives you a general idea

I think all true optimizers start by finding the maximum volume of a tuna can.

>>12301172
Learn the physics, chemistry, biology, economics or whatever is involved with the phenomena you want to model. Then you think which equations are involved with the specifics of the process you're trying to model. Then you put the equations together and get to some data to see if your model can explain the data.