Quantum mechanics

Algorithm

Gaussian

Statistics

Curse of dimensionality

Weighting

Computer science

Acoustics

Filter (signal processing)

Statistical physics

Physics

Mathematics

Sampling (signal processing)

Artificial intelligence

Monte Carlo method

Artificial neural network

Computer vision

Science

Efficient sampling of equilibrium states Molecular dynamics or Monte Carlo methods can be used to sample equilibrium states, but these methods become computationally expensive for complex systems, where the transition from one equilibrium state to another may only occur through rare events. Noé et al. used neural networks and deep learning to generate distributions of independent soft condensed-matter samples at equilibrium (see the Perspective by Tuckerman). Supervised training is used to construct invertible transformations between the coordinates of the complex system of interest and simple Gaussian coordinates of the same dimensionality. Thus, configurations can be sampled in this simpler coordinate system and then transformed back into the complex one using the correct statistical weighting. Science , this issue p. eaaw1147 ; see also p. 982

Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning