One challenge of physics is to explain how collective properties arise from microscopic
interactions. Indeed, interactions form the building blocks of almost all physical theories and …

The analysis of complex physical systems hinges on the ability to extract the relevant
degrees of freedom from among the many others. Though much hope is placed in machine …

Conventional studies of causal emergence have revealed that stronger causality can be
obtained on the macro-level than the micro-level of the same Markovian dynamical systems …

We apply the static auxiliary field Monte Carlo approach to study phase correlations of the
pairing fields in a model with spin-singlet pairing interaction. We find that the short-and long …

Real-space mutual information (RSMI) was shown to be an important quantity, formally and
from a numerical standpoint, in finding coarse-grained descriptions of physical systems. It …

Modern neuroscience has evolved into a frontier field that draws on numerous disciplines,
resulting in the flourishing of novel conceptual frames primarily inspired by physics and …

The fruits of science are relationships made comprehensible, often by way of approximation.
While deep learning is an extremely powerful way to find relationships in data, its use in …

For performing regression tasks involved in various physics problems, enhancing the
precision or equivalently reducing the uncertainty of regression results is undoubtedly one of …

We develop a machine-learning renormalization group (MLRG) algorithm to explore and
analyze many-body lattice models in statistical physics. Using the representation learning …

The Neural Network Field Theory correspondence (NNFT) is a mapping from neural network
(NN) architectures into the space of statistical field theories (SFTs). The Bayesian …