Machine learning and serving of discrete field theories - Scientific Reports
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A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach of learning discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton’s laws of motion and universal gravitation. The proposed algorithms are expected to be applicable when the effects of special relativity and general relativity are important.

One of the first papers by a serious physicist I’ve seen that is modeling their work on the premise of being in a simulation:

It has long been theorized since Euclid’s study on mirrors and optics that as the most fundamental law of physics, all nature does is to minimize certain actions. But how does nature do that? The machine learning and serving algorithms of discrete field theories proposed might provide a clue, when incorporating the basic concept of the simulation hypothesis by Bostrom. The simulation hypothesis states that the physical universe is a computer simulation, and it is being carefully examined by physicists as a possible reality. If the hypothesis is true, then the spacetime is necessarily discrete. So are the field theories in physics. It is then reasonable, at least from a theoretical point of view, to suggest that some machine learning and serving algorithms of discrete field theories are what the discrete universe, i.e., the computer simulation, runs to minimize the actions.

  • (relevant paragraph from the paper)

The central hypothesis of a discrete spacetime is rather conservative of a leap, but the looser hypothesis which is hinted at here (and one that’s very much been on my mind recently) is the notion that machine learning is behind aspects of natural processes.