Domain Decomposition Algorithms for Neural Network Approximation of Partial Differential Equations
摘要
With the success of deep learning technology in many application areas, there have been pioneering approaches to approximate solutions of partial differential equations by neural network functions [2, 10, 12, 13]. Such approaches have advantages over the classical approximation methods in that they can be used without generating meshes adaptive to problem domains or developing equation dependent numerical schemes.