Neural Network for Holographic QCD

Holographic QCD provides a powerful theoretical framework for investigating the equation of state of boundary field theories, where the idea is that the boundary dynamics can be fully determined by solving the bulk equations of motion. However, the coupling functions in the action typically rely on external inputs (such as lattice QCD data), and their explicit forms are often based on artificial assumptions. To eliminate such arbitrariness, we introduce neural networks into the potential reconstruction framework to represent the coupling functions, thereby constructing a fully data-driven machine learning model governed solely by boundary field theory inputs. The results obtained after training show remarkable consistency with the coupling functions derived from holographic renormalization based on prior assumptions, highlighting the strong function-approximation capability of neural networks and revealing the potential to unify the potential reconstruction and holographic renormalization approaches within a common framework.