How A.I. Answered the Real World Physics Problems

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Physics is one of the fields in science that humans still could not solve some of the biggest problems. Even these things are impossible for humans to solve. It is not all about the difficulty, but it can be time-consuming. Therefore, Artificial Intelligence becomes the new hope for humans to solve these problematic researches as below.

Particle Physics

Basically, we can use AI to solve high-energy physics problems. Higgs boson particle or God Particle is one of the biggest discoveries in physics. So, physicists discovered this particle by using the neural network. They did the researches at the Large Hadron Collider (LHC). They need to handle millions of data per day and analyze them manually. Of course, it is a tedious task. Additionally, particles like Higgs boson and others lie in this data. Annealer, a processor of quantum computer helped LHC so they could detect the particle. The neural network could detect the patterns in the particle collision. In short, the absence of AI made the discovery is almost impossible to handle. These particles have short lifetimes that they decay very fast.

Statistical Physics

One of an experiment that won a Nobel Prize in 2001 was Bose-Einstein. Physicists then repeated this experiment in 2016 but they used AI to help. It was incredible to know that the AI could complete the whole experiment for less than an hour. In this experiment, AI could make precise measurements such as applications and gravity. They developed optical lattice.


Another Nobel Prize winner was Gravitational Waves. It was one of the biggest discoveries in Astrophysics, recently. They found more Gravitational Wave signatures after involving AI with deep learning algorithms.
They also used neural networks to discover gravitational lensing, bending of light caused by gravity. Additionally, researchers have created convolution neural networks, and they can analyze images. As a result, this research can answer the questions in the most mystifying event of dark matter.

Nuclear Physics

Neural networks are also useful to represent ground state wavefunctions. They could implement various algorithms, learned about machine tools and others. Therefore, by using AI algorithms like artificial neural networks along with vector machines, researchers can develop nuclear physics properties. For example, they are an atomic mass number, parities, neutron capture rates, neutron separation energies, and others. In addition, neural network has contributed to identifying electrons.

Quantum Mechanics

Another breakthrough achieved by AI and physics is Quantum computers. This is the most powerful computers in the world. In fact, neural networks are able to represent ground state wavefunctions.

Material Science

Researchers from Northwestern University found three new glass-forming systems. But, they would be nothing without AI. These experiments required a lot of hours. But, they used AI algorithms so they could reveal about glass forming systems faster than you could expect. It fastened the process of detecting the new particles. So, they could save a lot of time during the experiments. Besides, similar algorithms could help NanoScience. The tools from UNSILO made Springer Nature got more data for NanoScience to discover new materials.

Atmospheric Physics

Using AI in Atmospheric Physics is about using algorithms such as neural networks, Fuzzy logic, Decision trees, and a subset of AI. A physicist in this field could not do much without using AI. AI was involved to answer some problems like identifying cyclones or understanding the mechanism of pollution. Physicist used algorithms of Ai such as clustering, self-organizing maps, and others. So, they used AI to make their research quicker and more reliable.
Even though AI supported a lot of discoveries in Physics, but physics enhance the AI methods in many ways. Quantum computers are the real evidence. So, researches complement each other in an amazing way. There are a lot of methods physicist have learned in the sector of AI, such as the fundamental laws of physics. This is a beneficial way to answer more problematic objects and experiments in science and technology.