Architecture of full-analogue photonic AI for non-standard problem solving

Some non-standard physical problems are challenging to solve because of the fundamental impossibility of experimental confirmation of theories, resulting from the lack of adequate methods and equipment with the required parameters, such as energy and frequency on the order of Planck. In recent years, generative artificial intelligence (AI) has significantly enhanced the efficiency of solving many standard problems, particularly in fields such as diagnostics, business analytics, pattern recognition, programming, and prediction. There are also attempts to leverage AI to address complex physical issues through indirect approaches, such as simulating a training dataset. This article aims to formulate the basic requirements that an AI system must meet to support the solution of non-standard physical problems that are also complex in their interdisciplinarity, data scarcity, time constraints, energy limitations, and hypothetical goals. This was conducted as a thought experiment by analysing two hypothetical phenomena: the emergence of cosmic strings (CS) and photons during the Planck and Grand Unification epochs. The author’s convergent method, based on thermodynamics and inverse problem-solving in topological space, has ensured the research’s stability and purposefulness. As a result of the article, it is justified that, to significantly improve AI support for the solution of some scientific non-standard problems, it is necessary to use the full-analogue photonic AI, which can be realised on a holographic basis and a Fourier transform approach. The conceptual architecture of a required AI system is represented.