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Thanks for the kind words! Good luck with your future courses!

Just updated the links, thanks for letting me know!

Hello, most videos on this channel are related to the course. I'm in the process of updating my Github to include a syllabus/video viewing sequence, but that probably won't be done for a while.

Hope it went well!

Please refer to the MathWorks debugging pages -- there could be many causes of this error. My code is attached in the video description and they should run error-free.

Hello, I don't have any 2DOF mass-spring-damper examples, but MathWorks does (www.mathworks.com/help/simscape/ug/double-mass-spring-damper-in-simulink-and-simscape.html) and this page on the File Exchange seems reputable (www.mathworks.com/matlabcentral/fileexchange/159391-double-mass-damper-mechanical-system)

Thank you very much!

Check out this paper, which is a modification of the SIR Model: www.sciencedirect.com/science/article/pii/S2468042720300725 In particular, it demonstrates that the alpha/beta constants traditionally used in the SIR Model are not actually constant, and a change is proposed to encompass these findings. In essence, the alpha/beta parameters are changed to be a function of the population density, among other things. Perhaps you can make a similar modification.

Appreciate it!

Absolutely! Good luck!

fixed

@@jaisohnkimVT Thank you so much for the lectures. I work in the automotive domain and your concepts are very helpful for learning automotive in a mathematical perspective.

Just fixed, thanks!

thanks, glad it helped!

I haven't tried adding noise to the simulations myself. I will refer you to these pages which may be helpful: www.mathworks.com/matlabcentral/answers/1646055-what-is-the-way-of-solving-the-noise-added-differential-equations-in-matlab, stackoverflow.com/questions/10343868/differential-equation-with-gaussian-noise-matlab

The links seem to work on my end, try again?

just came across in my search of vector norms and was astonished, 🐐. Makes me wonder why is it that some people can communicate ideas clearly without fluff but no loss of meaning while others can spend 40mins basically saying nothing

Thanks for watching. Unfortunately, I don't plan to make any Simulink-related videos anytime soon :(

@@jaisohnkimVT ayy it's okay man you've done good tutorials already :)

I assume you already have the S, I, and R data and you are attempting to find the a and r parameters which best fit the data. This is off the top of my head so I haven't tested this out at all, but perhaps something like the following might be a useful starting point: a_and_r = lsqcurvefit(@sir, initial_parameter_guesses,t,SIRdata,<other arguments>) function y = sir(a_and_r,t) % set up system of ODEs dydt = @(t,y) [-a*y(1)*y(2); % ...and so forth] % ode45 call [~,y] = ode45(dydt,t,initial_SIR_values); end

Glad my videos helped!

Recall that m*xddot + c*xdot + k*x = u, so the acceleration (xddot) is: xddot = (-c/m)*xdot - (k/m)*x + (u/m) Hopefully this is familiar from when we put the system into state-space form. Therefore, you can do something like: [t,x,v] = ode45(stuff); a = (-c/m)*v - (k/m)*x + (u/m); figure plot(t,a) This is the most "proper" way in my mind. You could alternatively obtain the accel vector by numerically differentiating the velocity vector, although I am not a fan of this because it could introduce noise into the accel vector. Hope this helps!

Hello, feel free to contact me at jais0hn@vt.edu, but please bear in mind that I cannot offer any consulting, model advice, debugging, etc.

Hi, I have a question. Why when susceptible individuals are equal to recovered individuals the infected individuals are maximum

The infected population doesn't always peak right when susceptible population = recovered population; it largely depends on the system parameters. I found this website (people.wku.edu/lily.popova.zhuhadar/) useful.