Gravitational Duals from Equations of State II: Large Hierarchies and False Vacua
2026-06-29 • Artificial Intelligence
Artificial IntelligenceMachine Learning
AI summaryⓘ
The authors study a way to understand complex quantum systems by finding their 'holograms' in gravity, focusing on tricky situations where the system has many energy scales and unusual behaviors. They improve a machine learning method called Physics-Informed Neural Networks (PINNs) to better guess the hidden properties of these systems from limited data. Their new techniques handle tough challenges like very close energy states and unseen parts of the system, allowing for accurate reconstructions even in difficult cases. This work shows how combining physics and machine learning can help explore strongly interacting quantum fields.
holographic dualitystrongly coupled quantum field theoryfalse vacuumgauge/gravity dualityrenormalization group flowPhysics-Informed Neural Networks (PINNs)scalar potential reconstructionthermodynamicsmachine learning in physicsenergy scale hierarchies
Authors
Raul Jimenez, David Mateos, Pavlos Protopapas, Pau Solé-Vilaró, Pedro Tarancón-Álvarez, Pablo Tejerina-Pérez
Abstract
We investigate the reconstruction of holographic duals for strongly coupled quantum field theories in regimes characterized by large hierarchies and the presence of false vacua. Within the gauge/gravity duality, these features translate into non-trivial thermodynamic behaviour and exotic renormalization group flows, including skipping flows between non-adjacent fixed points. Building on previous work based on Physics-Informed Neural Networks (PINNs), we extend the holographic inverse problem of reconstructing the bulk scalar potential from boundary thermodynamic data into this new regime. This setting presents a variety of conceptual and numerical challenges, such as near-degenerate states, large hierarchies of energy scales, and regions of the potential that are not directly probed by the input data. We develop a set of methodological advances that overcome these obstacles, thereby improving the established PINNs-based methodology and extending it to new physical regimes of interest that were previously out of reach. Applying the developed framework, we demonstrate accurate reconstruction of scalar potentials deep into the false vacuum regime, achieving robust agreement with the physical features of the underlying thermodynamics despite significant numerical stiffness. Our results extend the bridge between holography and machine learning, and suggest that data-driven approaches can provide new insights into the structure of strongly coupled systems.