Machine learning-assisted multi-scale modeling

Neural network-based machine learning is capable of approximating functions in very high dimension with unprecedented efficiency and accuracy. This has opened up many exciting new possibilities, one which to use algorithms assist multi-scale modeling. In this review, we three examples illustrate the process involved using modeling: ab initio molecular dynamics, meso-scale models, such as Landau models generalized Langevin equation, hydrodynamic for non-Newtonian flows.