Data mining and probabilistic models for error estimate analysis of finite element method

摘要: In this paper, we propose a new approach based on data mining techniques and probabilistic models to compare analyze finite element results of partial differential equations. We focus the numerical errors produced by linear quadratic approximations. first show how error estimates contain kind uncertainty in their evaluation, which may influence even damage precision results. A model problem, derived from an elliptic approximate Vlasov-Maxwell system, is then introduced. define some variables as physical predictors, characterize they odds elements be locally "same order" accurate. Beyond example, proposes method compare, between several approximation methods, accuracy