In the past 15 years, statistical physics has been successful as a framework for modelling
complex networks. On the theoretical side, this approach has unveiled a variety of physical …

Modeling and inference are central to most areas of science and especially to evolving and
complex systems. Critically, the information we have is often uncertain and insufficient …

Complex systems must respond to external perturbations and, at the same time, internally
distribute information to coordinate their components. Although networked backbones help …

Dynamical reweighting techniques aim to recover the correct molecular dynamics from a
simulation at a modified potential energy surface. They are important for unbiasing …

Self-organization is frequently observed in active collectives as varied as ant rafts and
molecular motor assemblies. General principles describing self-organization away from …

Robots and animals both experience the world through their bodies and senses. Their
embodiment constrains their experiences, ensuring that they unfold continuously in space …

Recurrent neural networks have seen widespread use in modeling dynamical systems in
varied domains such as weather prediction, text prediction and several others. Often one …

Ever since Clausius in 1865 and Boltzmann in 1877, the concepts of entropy and of its
maximization have been the foundations for predicting how material equilibria derive from …

There is a growing effort in the “physics of behavior” that aims at complete quantitative
characterization of animal movements under more complex, naturalistic conditions. One …

This review discusses successful strategies and key open problems in the kinetic and
thermodynamic characterization of complex biomolecular systems by computer simulations …