Preface (Communications in Computational Physics, 2020)
Machine learning has been gaining recognition rapidly as a powerful computationaltechnique to address some of the most challenging problems arising from scientific andengineering computations (SEC) with promising results in simulations of biological andquantum systems, fluid dynamics, wave scattering, high dimensional PDEs, and inverseproblems, etc. This special issue contains 1 survey paper and 17 original research articleson recent developments in machine learning, especially deep neural networks, concerning both its theoretical and algorithmic aspects pertinent to SEC.