Accelerating gradient-based topology optimization design with dual-model
  neural networks
  Topology optimization (TO) is a common technique used in free-form designs.
However, conventional TO-based design approaches suffer from high computational
cost due to the need for repetitive forward calculations and/or sensitivity
analysis, which are typically done using high-dimensional simulations such as
Finite Element Analysis (FEA). In this work, neural networks are used as
efficient surrogate models for forward and sensitivity calculations in order to
greatly accelerate the design process of topology optimization. To improve the
accuracy of sensitivity analyses, dual-model neural networks that are trained
with both forward and sensitivity data are constructed and are integrated into
the Solid Isotropic Material with Penalization (SIMP) method to replace FEA.
The performance of the accelerated SIMP method is demonstrated on two benchmark
design problems namely minimum compliance design and metamaterial design. The
efficiency gained in the problem with size of 64x64 is 137 times in forward
calculation and 74 times in sensitivity analysis. In addition, effective data
generation methods suitable for TO designs are investigated and developed,
which lead to a great saving in training time. In both benchmark design
problems, a design accuracy of 95% can be achieved with only around 2000
training data.
