  Developing appropriate analytic-function-based constitutive models for new
materials with nonlinear mechanical behavior is demanding. For such kinds of
materials, it is more challenging to realize the integrated design from the
collection of the material experiment under the classical topology optimization
framework based on constitutive models. The present work proposes a
mechanistic-based data-driven topology optimization (DDTO) framework for
three-dimensional continuum structures under finite deformation. In the DDTO
framework, with the help of neural networks and explicit topology optimization
method, the optimal design of the three-dimensional continuum structures under
finite deformation is implemented only using the uniaxial and equi-biaxial
experimental data. Numerical examples illustrate the effectiveness of the
data-driven topology optimization approach, which paves the way for the optimal
design of continuum structures composed of novel materials without available
constitutive relations.
