Hi I am a graduate student studying in fission theory. Do you guys have any recommended books on group theory?

so what will happen to a ball kept in a isolated system, will it dissipate over time and will mix homogeneously with the rest of the system?

Thanks for the video. Your recomendations are very much in line with my opinions. Personally, I use Julia for all my scientific computing tasks. I did like Matlab, but compared to Julia it is slow, expensive and does not have the easy access to packages and community that Julia has. And I much prefer the syntax of Julia as well.

What playlist is this video included? This one is not in your Classical Statistical mechanics list. It's frustrating to follow if we don't know the previous one.

thank you man ❤

Thx for the video

But in mathematics, there seemly be 2-order or higher order phase transitions. What are these, then? I am kind of confused.

WHAT THE BEST PRACTICAL QUANTUM PHYSICS AND STATISTICAL PHYSICS TEXTBOOKS THANK YOU

Statistical mechanics was originally based on what we may call a billiard ball model of monatomic gasses. The quantum mechanical revolution has altered this conception of “particles” such that we now model them as waves. Does your thesis take this into account? More importantly though, thermodynamics (including the second law) was originally a theory about the dynamics of heat and an increase in entropy over time originally meant simply that heat always flows from hot bodies to cold bodies. If we can explain the temporal asymmetry of net energy transfer in terms of more fundamental principles of black-body radiation, then can’t we discard explanations that depend on information, probability, and randomness as redundant? I would say yes.

what is k

This is such good production 😊🎉

At 16:22, what is the difference between ++m or m+=1 ??

my experience with R: the language itself is very annoying for how it's OOP works (prototypical like in JS) -- it's hard to find function's source code and hard to understand the call graph. (generally, i loathe OOP, it's a garbage solution to a non-existent problem). another problem is how the meta-programming stuff works but hopefully you'll never venture there. one thing that R is good at is regression-related models and statistical tests (but it's actually good at that). R also has a very strange way of doing some basic stuff (field access, array manipulation), but that's mostly a skill issue territory, and you get used to it.

how do we pronounce | psi(t) > as the straight line and triangle thing ?

I would like to learn this framework in chemistry. I think I found my motivation now. Thanks, sir

Hey Jonathon, I have a Bachelor degree in physics from the university in Eindhoven and am now in the second year of the Master Science and technology of Nuclear fusion. I am deeply passionate about physics and particularly fusion plasma physics (at the moment) and how to achieve a net energy producing fusion reactor as fast as possible. I have always been someone who wants to understand everything. During my bachelor I have always felt that I knew a lot of stuff about physics and math, but that I was lacking the actual skills and very restricted to analytical solutions. I am doing an internship at the Barcelona Supercomputing Center. I have learned some basics about Python and very little MATLAB before, but in this project fortran code is used. So I am learning a lot! As well about FEM in this case for EM wave propagation simulations. I have come to the conclusion that the skill (not the only one, but very important) I need to actually be able to apply my physics knowledge is computational modeling. But I am a firm believer, that real life experiments/practical implementation is what it is about eventually. At least for my fusion energy vision. And I love experimental projects very much as well. So from now on I am going to try to figure out what the synergy is between computational modeling and practical experiments and how this approach can be optimized for results and progression. This excites me very much! I actually came across your channel a few days ago while I was having this revelation. What is your view on the synergy between real life experimenting and modeling? And do you maybe have any tips for me along this journey? I know it is gonna take a lot of effort to be in this middle ground and still try to be super good at both areas, but it excites me a lot. I know that I can find a lot of stuff on the internet and it is just a question of doing and learning! Sorry for the long essay, but I wanted to share this story and get your view. Thanks!

Anyone want to weigh in on Jupyter vs Pluto.jl?

Nice! Sorry I just wanted to be like them... I noticed the dots that make up the line are spaced further apart at the beginning, what does that mean? Is it like field lines for a vector?

Extraordinarily clear and concise intro the reasoning of the born and markov approximations. Great example too! :)

Uniquely clear description of What entropy IS.

Wouldn't A interaction be defined as U A U dagger the other way around since we initially started with U dagger????

Related to the "information scrambling" video, is Anderson localization one way to prevent the scrambling of quantum information? Can we fully reconstruct the history of the state at the center of the lattice?

In the Aubrey-Andre model, can we predict where the localized sites would be?

Can information scrambling be de-scrambled? or is it fundamentally irreversible (thermo 2nd law connection)?

Can "scrambling propagation speed" covering larger and larger subsystem be defined as a meaningful quantity?

Is the scrambling of information inevitable in a many-body quantum system? How does system size play into this?

Hello, thank you for this video. I had a question. I am currently a CS student, and I am exploring all fields related to my expertise, and currently I am so curious about this path, computional science; But I don't find any good video. I want to know about one's experience who really worked something. Can you please fill a video and explain what is and how is your job, nad how can we enter this field?

This reminds me of a science fiction book I just read called quantum xy, about a group of Silicon Valley engineers who sought to create an ai learning model with moralistic principles. The system learned from the input commands of known moralists and grew its network based on the networks of these individuals. I can’t remember the author but I believe it was released in 93’ early 93. Way ahead of its time.

MatLab kinda slow

Amazing

I just got my bachelor in Fundamental Physics, and i chose to continue a Master degree in Computational physics, we'll start in September

hello, thanks for the vid! For reference, Fortran dependency management is getting better. See, for example, the fpm package manager, in development by the fortran-lang group. Just in case anyone wants to learn more. Fortran ftw!

Oh yes, didnt probability came way later after algerbra and like a bunch of really advanced maths.

Very well described. But anyways, it is frankly astounding that we can make accurate predictions about so many complex systems by using simple arguments in statistical mechanics!

Very nice video! I am currently a physics graduate student who is trying to get some knowledge of the computational methods. This subject is really intimidating at the early stage of learning. I really liked to hear different people describing them in they own way., which always give a me another perspective to see them. Hope I could get into my Phd in the future and help others like you did! Two questions, 1. at 18:12, is there should also be a exponential Lamda for |Emax> outside the bracket? 2. How could subtract a large enough C from the Hamiltonian helps to guarantee what we find is the ground state? Could you elaborate a little more?

thx for the nice explanation and greetings to your cat

@4:00 note that a lot of the pre-theoretical difficulty in quantum → classical comes from this (I think false or misleading) language framing. The Schrödinger equation does *_not_* describe the time evolution of the real physical system. It describes the effective time evolution of the Hilbert space "state" that is our model for the system. You can impose a realist view then, just do not take the ket as "the real".

As a scientist programmer, I really disagree about the comments over C. Although it is not usually the way-to-go, nothing is better than C. Nothing is faster than C. Nothing is more computationally efficient than C. So being able to delegate certain parts of your tasks to C code written by you is sometimes the most reasonable choice.

Thank you very much for all this. Burning out is the major challenge for PhD students. Really great advice! Going through the fastest path is usually the most reasonable choice, for sure. Nothing to add, great video!

15:01 This assumption is weird for me because it almost assume thos A_{m,m} are constant, so no matter what kind of measure( c_{m,m} or uniform) you use to compute expected value, the result is the same. So it's almost trivial because we can use what ever disdtribution we want, not necessary those given by stat mech. Or maybe there's more detailed argument about this assumption?

guru thanks for teaching ❤️

thanks for the very interesting video. Why is temperature defined via 1/T=dS/dE, would it be easier to say T=dE/dS? Is it because entropy changes as energy changes but not necessarily the other way around? Another words entropy is the dependent variable.

Don’t mix 3-body problems with probability 😊

amazing video, gave me clarity on the topic. Thanks for the help!

Python is similar to a markup language. If you have a problem, you say to python call the library written in another language. That means you have to learn python and a some libraries. One for calculation, one for visualization at least. You talked about the wired point syntax. Actually it is a strength of Julia.

Just come across negative temperatures seen several explanations after watching this things just went click, nice one!

Julia can call pyplot... Moreover, you get the ITensors library. Many Body Physics and Julia hands down!

Julia is absolutely great, if you don't need (or want) to rely on packages written by others. Its dynamically typed code, when used in functions, is super fast (one does not need to declare types, just like in Python or MATLAB), while its type system is used to extend functions or create "smart" data types. There is an extensive plotting suite, called Makie, that allows you to make multi-panel plots and interactive dashboards. You can also use notebook workflow. One slight disadvantage of Julia is its "time to first plot", waiting after starting up Julia and running the code for the first time, which used to be a big issue but has been improved a lot since v1.9. However, if you keep the REPL open, subsequent execution works instantly. I chose to learn Julia after coming from C, IDL/PV-WAVE and Scilab. Julia got the necessary packages I needed to get going with my data. I considered that I rather wanted to gain expertise in the best language for my job, than feel a beginner in Python working around slow and possibly unappetizing code already written by colleagues. Although most of my colleagues still remain on the fence as MATLAB and Python users, one of them let me know he tried out Julia and was pleased to find Julia can actually still interact very smoothly with Python (via PyCall).

Question, how much does this conservation of information theoretically applies to properties that are more familiar to a human scale. For example, let’s say you have two glasses of pure water and you mix them. Could you theoretically derive, through quantum mechanics, all the particular atoms which made of each glass?

The issue is vast majority of developers do NOT do novel things. I worked for amazon for many years, trust me