AI-ming for a Theory of Everything: Using AI to Illuminate Physics Research in 2020
AI-ming to a Theory of Everything
Year 2020 o.o.
In the modern world, a variety of tools are used to reveal the foundations of reality, ranging from machine learning to artificial intelligence. Physics has struggled to find a theory of everything. A grand, unified theory that encompasses all of nature and physics. While unifying various forces and interactions in nature, starting from the unification of electricity and magnetism in James Clerk Maxwell’s seminal work A Treatise on Electricity and Magnetism  to the electroweak unification by Weinberg-Salam-Glashow [9-11] and research in the direction of establishing the Standard Model including the QCD sector by Murray Gell-Mann and Richard Feynman [12,13], has seen developments in a slow but surefooted manner, we now have a few candidate theories of everything, primary among which is String Theory . We are not yet able to establish the various aspects of the theory empirically. The concept of supersymmetry is a key part of String Theory. The first run of the Large Hadron Collider did not find any evidence for supersymmetry . The results of the Large Hadron Collider’s discovery of the Higgs Boson were not good for the Minimum Supersymmetric Model. This is because the Higgs Boson mass at 125 GeV was too large for this model. It could only be achieved with large radiative-loop corrections by top squarks, which many theoreticians deemed to be \”unnatural\” . The importance of computational research and simulations cannot be underestimated in the absence of experiments which can test certain frontiers of Physics. This is due to energy restrictions, especially at the smallest scales. Isaac Newton was supposedly able to sit under an apple tree, and deduce the concept of classic gravity by observing an apple fall on his head. Today, the computational power and inputs are increasing. This is important when evaluating new avenues for research in Physics. M-Theory , introduced by Edward Witten, is one promising approach towards a unified Physics model that includes quantum gravitation. It extends the formalistics of String Theory. Recently, computational tools related to machine learning have been used to solve M-Theory geometry . TensorFlow is a machine-learning computing platform that has been used to find 194 solutions of equilibrium for a particular type M-Theory geometries.
Artificial intelligence is a major area of interest for computational pursuits in Physics research. Matsubara Takashi, Yaguchi Takaharu, and their research team at Kobe University, along with the help of artificial intelligence, succeeded in 2020 to develop technology that can simulate phenomena we don’t have detailed formulas or mechanisms for. This step is based on the creation of an observational model, which must be consistent with the laws of Physics. The researchers used digital calculus and geometrical approaches, including those of Riemannian Geometry and symplectic geometries, to achieve this goal.