  Conventional neural network elastoplasticity models are often perceived as
lacking interpretability. This paper introduces a two-step machine learning
approach that returns mathematical models interpretable by human experts. In
particular, we introduce a surrogate model where yield surfaces are expressed
in terms of a set of single-variable feature mappings obtained from supervised
learning. A post-processing step is then used to re-interpret the set of
single-variable neural network mapping functions into mathematical form through
symbolic regression. This divide-and-conquer approach provides several
important advantages. First, it enables us to overcome the scaling issue of
symbolic regression algorithms. From a practical perspective, it enhances the
portability of learned models for partial differential equation solvers written
in different programming languages. Finally, it enables us to have a concrete
understanding of the attributes of the materials, such as convexity and
symmetries of models, through automated derivations and reasoning. Numerical
examples have been provided, along with an open-source code to enable
third-party validation.
