Advances in machine learning methods provide tools that have broad applicability in
scientific research. These techniques are being applied across the diversity of nuclear …

In recent years, machine learning has emerged as a powerful computational tool and novel
problem-solving perspective for physics, offering new avenues for studying strongly …

Over the past few years, machine learning has emerged as a powerful computational tool to
tackle complex problems in a broad range of scientific disciplines. In particular, artificial …

Neutron and x-ray scattering represent two classes of state-of-the-art materials
characterization techniques that measure materials structural and dynamical properties with …

A bstract We employ a deep learning method to deduce the bulk spacetime from boundary
optical conductivity. We apply the neural ordinary differential equation technique, tailored for …

A bstract We investigate the bulk reconstruction of AdS black hole spacetime emergent from
quantum entanglement within a machine learning framework. Utilizing neural ordinary …

We construct a neural network to learn the Reissner-Nordström-anti–de Sitter black hole
metric based on the data of optical conductivity by holography. The linear perturbative …

We show a new family of neural networks based on the Schrödinger equation (SE-NET). In
this analogy, the trainable weights of the neural networks correspond to the physical …

We introduce a continuous depth version of the Residual Network (ResNet) called Neural
ordinary differential equations (NODE) for the purpose of galaxy morphology classification …

A bstract A construction of a gravity dual to a physical gauge theory requires confronting
data. We establish a proof-of-concept for precision holography, ie, the explicit reconstruction …