Speaker
Prof.
Hyun-Sik Jeong
Description
Holography (AdS/CFT) provides a powerful framework for studying the quantum nature of gravity and strongly coupled quantum systems. This talk showcases how deep neural networks can address inverse problems in holography: specifically, reconstructing bulk gravity models from boundary observables. By integrating holography with physics-informed neural networks, we show that strongly coupled systems, from QCD-like theories to condensed-matter models and entanglement-based setups, can be analyzed in a data-driven and robust way. The aim is to demonstrate how machine-learning methods enable stable, consistent reconstructions and offer new insights that complement traditional holographic approaches.
