Main article: Artificial Intelligence
How AI is used in science
Generative AI can have a fundamental impact on science, technology, accelerating technological progress. In 2023, Spydell Finance identified several key areas in this area.
Systematization and structuring of ultra-large arrays of information
How can a scientist find similar research materials? Through search, but the data can be irrelevant or obsolete. Indexing and analysis of thousands of scientific articles is needed to combine and integrate similar studies into a holistic picture.
Hypothesis generation
AI can be used to generate hypotheses that can be used to conduct scientific research. This could help scientists speed up the process of discovering new knowledge.
Ultra-fast search and processing of solution combinations to find the optimal research path
AI can help scientists design and optimize experiments by predicting the most promising areas of research, which reduces costs and increases the chances of success.
Simulation and Simulation
AI is able to create complex models and simulations that can predict the results of experiments and research, as well as help in understanding complex systems and processes.
Quickly find and correct errors in mathematical, physical models, or program code
Quickly finding and correcting errors in mathematical, physical models or program code will simplify and speed up the calculation process.
Analysis and interpretation of complex data in modeling complex systems
Analysis and interpretation of complex data in the modeling of complex systems, creating a more understandable and readable data structure.
2022: AI simplified the solution of the well-known problem of quantum physics from 100 thousand equations to four
On October 4, 2022, it became known that with the help of artificial intelligence (AI), physicists were able to radically optimize the known quantum problem, which until recently implied the solution of 100 thousand different equations. Now it is enough to solve the four equations, and this is without any sacrifice in terms of the accuracy of the results.
As reported, the work, published in Physical Review Letters on September 23, 2022, could lead to changes in how scientists investigate systems containing multiple interacting electrons. If this solution can be scaled to other similar problems, it will be possible to create superconducting materials or means of environmentally friendly energy production.
 | We start with a large corpus of interconnected differential equations and then use machine learning to turn it into something so small that you can count it on your fingers. |  |
The problem, known as the Hubbard model, relates to the behavior of electrons moving inside a lattice structure. If two electrons occupy one point in the lattice, they interact. Hubbard's model is an "ideal" variant of several important classes of materials; with it, scientists gain insight into how the behavior of electrons provides the desired states of matter, such as superconductivity, in which electrons move without encountering resistance. The model is also used to work out various methods of working with more complex quantum systems.
The simplicity of Hubbard's model, however, is deeply deceptive, the publication writes Phys.org. Even when a modest number of electrons are calculated, and the most advanced computational approaches are used, the amount of computation itself remains large. The point is quantum coupling: after two electrons interact, they are coupled, and no matter how far apart they are subsequently, they cannot be considered as independent units. As a result, physicists have to take into account all electrons at once, and not each individually. And the more electrons are added to the system, the more clutches occur, and the higher the computational resources that are required to study such a system.
In such cases, physicists use renormalization groups - a mathematical apparatus that is used to detect changes in the system when modifying its properties, such as temperature, or the consequences of scaling.
However, even a renormalization group tracking all possible couplings between electrons without compromising accuracy would contain tens of thousands, hundreds of thousands, or even millions of individual equations to be solved.
Di Sante and his colleagues thought about the possibility of using a neural network in order to make a massive renormalization group more manageable. And they succeeded.
The neural network first indexed all connections in the full-size renormalization group, then reconfigured the strength of these connections until it revealed a narrowly limited set of equations that produce exactly the same result as the original renormalization group. The number of such equations was eventually reduced to four.
Neural network training required large computing resources: the program worked continuously for several weeks. However, now this neural network can be used to produce calculations in connection with other major physical and mathematical problems, without the need to start its training from scratch.
Di Sante and his associates also study what exactly their neural network "understood" about the system to which it was applied, in the hope of revealing patterns that were not obvious to physicists before.
The question remains how much this approach works with more complex quantum systems, for example, with materials in which electrons interact at long distances. According to Di Sante, there are very interesting opportunities to use this method in other areas where renormalization groups are used, including cosmology and neurology.
| | If the conclusions made in this work are not refuted, then perhaps we are talking about a global revolution in physics. A revolution that turned out to be achievable only thanks to machine learning and the ability characteristic of neural networks to identify hidden patterns that allow complex systems to be reduced to a reasonable number of parameters. So far, the capabilities of neural networks are at the initial stage of development, but there is reason to believe that in the future they will be able to solve some other problems and problems of physics that still remain unresolved, for example, Schrödinger equations, multiple problems of superfluidity[1] | |
2020: Sberbank attracts artificial intelligence to decipher the manuscripts of Peter the Great
On June 29, 2020, it became known that Sberbank decided to attract artificial intelligence technologies to decipher the manuscripts of Peter the Great. Read more here.
Notes
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