/r/Physics
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/r/Physics
I am a 3rd year undergrad student and what intrests me the most in physics is its theoretical side. However, my university doesn't think that theoretical physics is important and teaches mostly experimental physics. This is especially visible when it comes to mathematical methods which are important for theoretical physics. So when I want to study more advanced topics like quantum field theory in many body or condensed matter, I find myself lacking in areas such as topology, group theory, tensor calculus or distributions. I want to understand physics and the math behind it on a deeper level, so any information on books or sources that could help me with learning the mentioned topics would be great.
Unfortunately my university follows a rather old and rigid method of organizing courses so I can not change any courses or pick up any new ones.
This thread is a dedicated thread for you to ask and answer questions about concepts in physics.
Homework problems or specific calculations may be removed by the moderators. We ask that you post these in /r/AskPhysics or /r/HomeworkHelp instead.
If you find your question isn't answered here, or cannot wait for the next thread, please also try /r/AskScience and /r/AskPhysics.
Is this because of the sketchy practices I’ve read about? I’ve been told never to publish in MDPI unless you’re already well established in your field as some of their journals have some credibility and editors.. anyone’s thoughts on this?
This is a thread dedicated to collating and collecting all of the great recommendations for textbooks, online lecture series, documentaries and other resources that are frequently made/requested on /r/Physics.
If you're in need of something to supplement your understanding, please feel welcome to ask in the comments.
Similarly, if you know of some amazing resource you would like to share, you're welcome to post it in the comments.
As an EMT nerd, my suggestion is Electromagnetic vortex cannon
It might be more inclined to RF engineering ( but defeatly a better choice than AI in my opinion )
It make closed-loop EM waves that might be able to travel a long distance without change in shape , I am not up to date with the physics discoveries in 2024 , for those who follow them
What are the experiments that you think deserved the Nobel price for physics in 2024 ?
Hi,
I'm curious about the topic of physically simulated sound generation. I'm wondering if there's an established equation or framework similar to the rendering equation in optics, but for sound. By "physically simulated," I mean generating realistic sounds based on the physical interactions and properties of materials, rather than using recorded samples or synthesized approximations.
For example, simulating the sound of:
A metal rod being struck
Ice being compressed or broken
Leather being rubbed or stretched
I know that some models exist for simulating musical instruments (like guitars, pianos, etc.), where things like string vibration and resonance are taken into account. However, I'm curious if there’s a more general approach or theoretical framework that covers a wider range of physical interactions for sound generation.
Is there an equation or set of principles that can be applied to these scenarios in a similar way that the rendering equation helps with light in optics? Or is it more about case-specific models for different types of materials and interactions?
Just curious and would love to learn more about this topic!
Thanks!
Note: this is a little bit of a ramble as I just wanted to get things of my chest, so apologies if this is a little long winded!
So for a bit of context: I'm a fresh first year PhD student (23M) in the field of laser/plasma physics, and in general my project is working on developing ICF as a possible way to produce fusion energy (yet to properly start the project so details are vague atm). I'm more on the side of theory and computation/simulation. I've always wanted to persue a physics PhD, and while I find the prospect of fusion energy really really exciting and the physics of ICF very cool, my mind has recently been plagued by thoughts of how ICF physics is very much related to the development of weapons, in particular nuclear weapons (NIF comes to mind as the primary example of this).
Now I knew this before going into the PhD, but recently (and I'm not sure why tbh), it's been much more on my mind. I've spoken to a lot of people about it; fellow PhD students, staff, my supervisor etc, and no body seems very phased by it. When my supervisor suggested working with NIF I told him I'm not too keen on the idea due to their link with weapons, and he said something along the lines of 'well the link to weapons will always exist' and kinda just brushed it off. While I don't disagree with this, I just can't help worry about it :/. My worries are probably quite irrational, but I don't like the idea of my potential work being used to develop a new and more dangerous nuke (not at all likely or even possible ik, but you get the idea).
So I just wanted to ask, how do physics researchers in fields closely linked to weapons sleep peacefully at night? I'm honestly starting to doubt I'm cut out for a field like this, which I'm happy to accept other than the fact that I really love physics/maths and really want to to a PhD. I feel like I'd be throwing away a really good opportunity, because every other aspect of the PhD (supervisor, fellow students, the uni, city etc) are pretty perfect.
P.S: Wasn't sure exactly which subreddit I should post this to (maybe r/PhD?) , so any suggestions on other subreddits would be appriciated!
This is a dedicated thread for you to seek and provide advice concerning education and careers in physics.
If you need to make an important decision regarding your future, or want to know what your options are, please feel welcome to post a comment below.
A few years ago we held a graduate student panel, where many recently accepted grad students answered questions about the application process. That thread is here, and has a lot of great information in it.
Helpful subreddits: /r/PhysicsStudents, /r/GradSchool, /r/AskAcademia, /r/Jobs, /r/CareerGuidance
Disclosure: JJ Hopfield is a pioneer in my field, i.e., the field of statistical physics and disordered systems, so I have some bias (but also expertise).
I wanted to make this post because there are some very basic misconceptions that are circulating about this year's Nobel Prize. I do not want to debate whether or not it was a good choice (I personally don't think it is, but for different reasons than the typical discourse), I just want to debunk some common arguments relating to the prize choice which are simply wrong.
Myth 1. "These are not physicists." Geoffrey Hinton is not a physicist. JJ Hopfield is definitely a physicist. He is an emeritus professor of physics at Princeton and served as President of the American Physical Society. His students include notable condensed matter theorists like Bertrand Halperin, former chair of physics at Harvard.
Myth 2. "This work is not physics." This work is from the statistical physics of disordered systems. It is physics, and is filed under condensed matter in the arxiv (https://arxiv.org/list/cond-mat.dis-nn/recent)
Myth 3. "This work is just developing a tool (AI) for doing physics." The neural network architectures that are used in practice are not related to the one's Hopfield and Hinton worked on. This is because Hopfield networks and Boltzmann machines cannot be trained with backprop. If the prize was for developing ML tools, it should go to people like Rosenblatt, Yann LeCun, and Yoshua Bengio (all cited in https://www.nobelprize.org/uploads/2024/09/advanced-physicsprize2024.pdf) because they developed feedforward neural networks and backpropagation.
Myth 4. "Physics of disordered systems/spin glasses is not Nobel-worthy." Giorgio Parisi already won a Nobel prize in 2021 for his solutions to the archetypical spin glass model, the Sherrington-Kirkpatrick model (page 7 of https://www.nobelprize.org/uploads/2021/10/sciback_fy_en_21.pdf). But it's self-consistent to consider both this year's prize and the 2021 prize to be bad.
If I may, I will point out some truths which are related to the above myths but are not the same thing:
Truth 1: "Hinton is not a physicist."
Truth 2: "This work is purely theoretical physics."
Truth 3: "This work is potentially not even that foundational in the field of deep learning."
Truth 4: "For some reason, the physics of disordered systems gets Nobel prizes without experimental verification whereas other fields do not."
Modeling physical systems:
Heck, we might even be on the way to a unified model - https://www.anl.gov/article/ai-technique-does-double-duty-spanning-cosmic-and-subatomic-scales
I could keep going but these are some of the most awe-inspiring things I've seen that come to mind when I think of NNs and Physics. It's changed the way I conceptualize a problem and I think the power is in that anything that can be formulated as a combination of an encoding and an objective can be modeled with a recipe that generalizes incredibly well. It's a new paradigm of problem solving and has and will continue to have impact similar to say something like calculus.
I feel like a lot of the outcry has to do with the computation being outsourced from blackboard to silicon, or because it's less "elegant" or "noble" than having a bespoke, mathematically and physically derived solution to a problem and that any codebro can do it, but that is the beauty of it. It's a new framework of thinking about problems. It's incredibly powerful and fast. To think that I can do on my RGB mountain dew desktop what only some professor with access to a supercomputer could do as recently as 10 years ago? The impact on physics is undeniable. Their work made it a free-for-all and that's the way it should be.
EDIT: My labmate saw me post this and we got to talking. Pulled up the list of previous winners. Look at these from 2021:
“for groundbreaking contributions to our understanding of complex physical systems”
“for groundbreaking contributions to our understanding of complex physical systems”
Syukuro Manabe and Klaus Hasselmann
“for the physical modelling of Earth’s climate, quantifying variability and reliably predicting global warming”
“for the discovery of the interplay of disorder and fluctuations in physical systems from atomic to planetary scales”
Neural networks can do the stuff people get Nobel prizes for.
This thread is a dedicated thread for you to ask and answer questions about concepts in physics.
Homework problems or specific calculations may be removed by the moderators. We ask that you post these in /r/AskPhysics or /r/HomeworkHelp instead.
If you find your question isn't answered here, or cannot wait for the next thread, please also try /r/AskScience and /r/AskPhysics.
I have been thinking about this for a while now, and the Nobel prize announcement triggered me to post this.
I have been applying to PhDs the past year, and I am mainly interested in cosmology. It feels like the AI/ML craze has especially taken over this field. In the past year, so many of the cosmology related positions involve deep learning and neural networks.
I understand to some extent, as computational powers limit simulations with baryons, and neural networks provide an alternative way to avoid these heavy simulations. But still, sometimes it feels like people are just going with the craze and adding Al to their research portfolios (and to get more funding as well).
I am not saying we should not use these new tools, but I guess the applications sometimes feel very on the surface.
What do you all think? How is it with other fields of research in physics?
Can someone explain what core concepts of physics are used in linking machine learning and artificial neural networks?
This is interesting...
I don't want to downplay the significance of their work; it has led to great advancements in the field of artificial intelligence. However, for a Nobel Prize in Physics, I find it a bit disappointing, especially since prominent researchers like Michael Berry or Peter Shor are much more deserving. That being said, congratulations to the winners.
Just a question. It took me a whole day to understand how light works based on inertial and accelerating perspectives. Even though I really like physics it seems like I'm too dumb to learn. Is there a possibility for me to get better.