  In solid mechanics, Data-driven approaches are widely considered as the new
paradigm that can overcome the classic problems of constitutive models such as
limiting hypothesis, complexity, and high dependence on training data. However,
implementation of machine-learned approaches in material modeling has been
modest due to the high-dimensionality of the data space, significant size of
missing data, and limited convergence. This work proposes a framework to hire
concepts from polymer science, statistical physics, and continuum mechanics to
provide super-constrained machine-learning techniques of reduced-order to
overcome many of the existing difficulties. Using a sequential order-reduction,
we have simplified the 3D stress-strain tensor mapping problem into a limited
number of super-constrained 1D mapping problems. Next, we introduce an assembly
of multiple replicated Neural Network agents to systematically classify those
mapping problems into a few categories, all of which are replications of a few
distinct agent types. By capturing all loading modes through a simplified set
of disperse experimental data, the proposed hybrid assembly of agents provides
a new generation of machine learned approaches that simply outperforms most
constitutive laws in training data volume, training speed, and accuracy even in
complicated loading scenarios. Also, it avoids low interpretability of
conventional AI-based models.
