  This paper introduces a new local plastic correction algorithm developed to
accelerate finite element simulations for structures with elasto-plastic
constitutive laws. The proposed method belongs to the category of generalized
multiaxial Neuber-type methods enabled by pointwise proportional evolution
rules. The algorithm numerically integrates J2 plasticity laws as a function of
the finite element elastic response of the structure, to obtain full-field 3D
elasto-plastic quantities for any proportionally applied loading. Examples of
the numerical capabilities of this algorithm are shown on a structure
containing a distribution of pores, for monotonic and fatigue loading. The
approximation errors due to the proposed local plastic correction are also
investigated. As a second point of innovation, we show that the proposed local
plastic correction can be accelerated when dealing with large-scale structures
by employing a simple meta-model, with virtually no added errors. Finally, we
develop and investigate the merits of an additional deep-learning-based
corrective layer to reduce approximations errors on a subset of structures for
which full elasto-plastic FE simulations are performed, the solutions of which
are subsequently used as training set for a Convolutional Neural Network
algorithm designed to learn the error between full FE and plastic correction
approximations.
