By Morgan Sherburne, University of Michigan
University of Michigan physicists have devised a way to manipulate active fluids, a type of fluid composed of individual units that can propel themselves independently, by taking advantage of topological defects in the fluids.
Black holes are intriguing astronomical objects that have a gravitational pull so strong that it prevents any object and even light from escaping. While black holes have been the topic of numerous astrophysical studies, their origins and underlying physics remain largely a mystery.
Chat gpt 4 is really excellent in physics work aiding the user very well much like wolfram alpha has done.
Artificial intelligence (AI) technologies have been consistently influencing the progress of education for an extended period, with its impact becoming more significant especially after the launch of ChatGPT-3.5 at the end of November 2022. In the field of physics education, recent research regarding the performance of ChatGPT-3.5 in solving physics problems discovered that its problem-solving abilities were only at the level of novice students, insufficient to cause outstanding alarm in the field of physics education. However, the release of ChatGPT-4 presented substantial improvements in reasoning and conciseness. How does this translate to performance in solving physics problems, and what kind of impact might it have on education?
Apologies for the (hopefully now somewhat less) clickbait-y title. Now, of course, I know that the Big Bang did not happen at any point connected to a single point in our current $3$-dimensional observable universe by a one-dimensional causal curve. I also know that at any point in the universe, all other points seem to be moving away from that point. However, according to our current understanding of physics, the universe is (at least) $4$-dimensional. Just like how in the classical “balloon” analogy for an expanding universe, the points do in fact all move away from a common point on the interior of the balloon, all spacetime points do move away from the Big Bang, or at least some kind of cosmological horizon which surrounds it — this is how I understand going forward in time, at least. Does it make sense to think of this as a sort of “center” for the full, $4$-dimensional spacetime? Or are there further subtleties to this situation?
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Einstein completed his theory of general relativity in 1915 when he was 37 years old. What did he do for the remaining 40 years of his life? He continued developing his masterwork of course! Feeling that his theory was incomplete, Einstein pursued a unified field theory. Though he ultimately failed, the ideas he came up with were quite interesting. I have read a lot of old Einstein papers in the past weeks and here is my summary of what I believe he tried to do.
Black hole singularities defy the laws of physics. New research presents a bold solution to this puzzle: Black holes may actually be a theoretical type of star called a ‘gravastar,’ filled with universe-expanding dark energy.
Photo : siqi zhao & huirong yan.
Astrophysicists from the University of Potsdam have made a significant step toward solving the last puzzle in magnetohydrodynamic turbulence theory by observing the weak to strong transition in the space plasma turbulence surrounding Earth with newly developed multi-spacecraft analysis methods. Their pioneering discovery was published today in the journal Nature Astronomy.
Turbulence is ubiquitous in nature. It exists everywhere, from our daily lives to the distant universe, while being labelled as “the last great unsolved problem of classical physics” by Richard Feynman.
For the first time, scientists have seen the small ripples that result from black holes’ motion, which are gently stretching and squeezing everything in the universe.
They revealed that they could “hear” low-frequency gravitational waves, which are produced by massive objects colliding and moving around in space and causing changes in the universe’s fabric.
“This is a really nice way of incorporating something you know about your physical system deep inside your machine-learning scheme. It goes far beyond just performing feature engineering on your data samples or simple inductive biases,” Schäfer says.
This generative classifier can determine what phase the system is in given some parameter, like temperature or pressure. And because the researchers directly approximate the probability distributions underlying measurements from the physical system, the classifier has system knowledge.
This enables their method to perform better than other machine-learning techniques. And because it can work automatically without the need for extensive training, their approach significantly enhances the computational efficiency of identifying phase transitions.
Despite its promising characteristics in condensed matter physics, the triply-degenerate semimetal PtBi2 has been largely unexplored in practical applications, particularly in semiconductor technology. The main difficulties include a lack of empirical data on the integration of PtBi2 with existing semiconductor components and the need for innovative approaches to leverage its unique properties, such as high stability and mobility, within the constraints of current electronic manufacturing processes.
