This is a high quality content. This is exactly what I was trying to do!! Thank you very much. Please keep posting more videos like this. I’m a big fan of your content!!!
Just graduated on Thursday, but I love learning and your videos feel like a continuation of Vibrations and Dynamic Systems. Hope this channel keeps on growing !
Another great video! Really liked the sympy stuff: I had no clue one can introduce approximations like that (like $sin(x) \approx x$ for small x) directly in sympy. Thanks!
Because we can then scale our problem to have dimensions afterwards. For example, you solve a dimensionless problem where the second pendulum arm is 4x longer than the first in dimensionless units. So your dimensionless lengths are (1,4). That means it applied to (1cm, 4cm) long arms (1m, 4m) long arms and even (10m, 40m) long arms. Nothing to do with numerical accuracy per se. That being said, if you were dealing with a problem that had really big units (e.g. you decided to measure your pendulum arms as 10^9 nanometers) then you may see some increase in numerical precision and speed for more complex algorithms (such as large matrix inversion). In other words, it's good to standardize based on the dimensions of your problem.
Hello..I am a PhD student in physics from Iraq..I hope you can help me find codes in the Python program to study the Fe(II)particle (ising model 2D)to determine the spin crossover of the electrons and find the energy..with many thanks to you.
This is really intersting. I loved Lagrangean mechanics at university. I tried to do something similar for a coupled string/spring pendulum as in "Superposition of oscillation on the Metapendulum: Visualization of energy conservation with the smartphone " or ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-Oyr7sBzs4cY.html . Unfortunately, I don't have much much experience in solving ODEs with python and the solver runs into problems. Maybe that would be a worthwhile project for the future. Thanks for the cool physics videos.
