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How Machine Learning Has Become A Part Of Every Physicist’s Toolbox


How Machine Learning Has Become A Part Of Every Physicist’s Toolbox


Physics, too, has fallen into the artificial intelligence hype with a clutch of researchers using machine learning to deal with complex problems regarding huge amount of data. ML applications in physics are becoming an important part of modern experimental high energy analyses. From high-energy physics to quantum physics and condensed matter, ML applications are upending physics-based modelling. According to an academic researcher, physicists are mostly using ML to accelerate calculations of atomic energies. ML has also been used for good effect in high energy experiments and observational astronomy. In this article, we are going to discuss numerous applications in condensed matter physics and how physicists are quick to embrace new tech to find breakthroughs.



Machines Make Way Into Physics

ML In Condensed Matter Physics

In fact, breakthroughs in condensed matter physics have been assiduously documented by well-known researcher Juan Felipe Carrasquilla, an expert in theoretical condensed matter physics, and numerical methods who said in an article that the most difficult problem lay in:

“Understanding the wavefunction of a many-particle quantum system with relevant accuracy which can pave the way for the designing of new quantum materials and devices.”

This led him to think that machines can resolve some of the biggest questions in physics. The article notes that researchers are using ML algorithms to understand the phases of matter, which could lead to theoretical breakthroughs in quantum bit or even silicon. Before the advent of ML, physicists relied on a bunch of tools for deriving observations. For example, the use of macro-models phased out the use of micro-parameters. Statistical modelling was the most basic tool used for deriving inference general patterns from data.

Deep Learning for Tracking in High Energy Physics

According to an ICLR 2107 research paper, deep learning techniques have been successfully applied to high energy physics. This paper applies deep learning techniques to tracking in high energy physics experiments. The researchers deployed an LSTM approach and proposed an end-to-end solution for the HL-LHC track pattern recognition challenge. The paper also explored the role of recurrent neural networks and long short-term memory networks in modelling these dynamics. In another paper titled, Searching for Exotic Particles in High-Energy Physics with Deep Learning, the researchers demonstrated how through benchmark datasets, deep learning techniques improved the classification metric by 8 percent.

ML In Astrophysics

Off late, ML methods have found application in astronomy and astrophysics for processing the wide amount of astronomical data generated which are often heterogeneous in nature. ML techniques have been applied for developing ways to process, analyse astronomical data in an automated manner. Other applications include using ML techniques to gain data-driven insights and visualize it further. Other approaches include developing scalable methods for automated learning. In fact, AstroML, ML for Astrophysics, a highly cited research paper introduces ML tools for statistical analysis. The astroML software package is available for free and provides researchers and students with resources like statistical tools and dataset loaders for Python implementation of statistical routines.

See Also

Universities Training Physicists In Deep Learning Techniques

Many universities are introducing computational techniques, especially neural networks used in a variety of applications such as pattern recognition, image recognition, natural language processing. Universities are covering the whole gamut of neural networks like autoencoders, recurrent neural networks, Hinton’s backpropagation technique and also some emerging applications for deep learning in Physics. The preferred programming language is Python and Matlab. Some of the most popular use cases for ML in Physics are analysing phase transitions and making predictions of material properties.

Why Use ML When We Already Have Physics-Based Models?

Ideally, a computational approach is always the best way to resolve a problem. Traditionally, physicists relied on statistical methods and physics-based model to tackle the question. Given this scenario, should one take an ML-based approach? A key drawback is the high cost of the computational model which can also be very time-consuming. Another aspect is that black box problem of ML where it is hard to understand the biases which can be a setback to validating the results. Another drawback is the huge amount of data required for ML-based methods and the cost associated with collecting data than they can use.



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