So many time steps are often needed to simulate a dynamical system’s final state that the computation becomes infeasible. Now researchers have used a machine-learning approach to extend the time steps in atomic-scale simulations by an order of magnitude.
physics.aps.org
Machine learning can reduce the number of time steps needed to accurately predict the progress of a dynamically evolving system.
