Stability Theory and the Foundations of Physics: A Challenge to Real Analysis and Numerical Calculation

TL;DR

This paper discusses the mathematical stability of a quantum field theory model, proposing a multi-stage proof strategy, and introduces a new numerical method for stability analysis relevant to theoretical physics and PDE systems.

Contribution

It proposes a novel multi-stage approach to prove the existence of solutions for a quantum field theory and introduces a new numerical stability checking method.

Findings

Proposes a multi-stage strategy for proving quantum field theory existence.

Introduces a new numerical method for stability analysis of PDE solutions.

Highlights the importance of stability in classical and quantum field theories.

Abstract

At present, there exists no physically plausible example of a quantum field theory for which the existence of solutions has been proven mathematically. The Clay Mathematics Institute has offered a prize for proving existence for a class of Yang-Mills theories defined by Jaffe and Witten. This paper proposes a multi-stage strategy for proving existence for a quantum version of the "'tHooft Polyakov" (tP) field theory, and argues that this theory, while not renormalizable or physically plausible as it stands, opens up a clear path to a physically plausible well-defined theory. The key initial challenge is to first prove stability for a classical version of this theory, in the spirit of Walter Strauss. The stability results of Bogomolnyi for classical PDE systems are widely cited as a foundation of string theory, but they leave key questions unaddressed, and may even call for small modifications of the tP model itself. This paper calls attention to the questions which require more attention, and proposes some partial possible answers. It also specifies a new numerical method to check for stability for the usual "hedgehog" types of solution, a method which may have broader potential uses for any ODE problems where accuracy in derivatives is as important as accuracy in function values.