FluxGAN: A Physics-Aware Generative Adversarial Network Model for
  Generating Microstructures That Maintain Target Heat Flux
Artem K. Pimachev Manoj Settipalli Sanghamitra Neogi
We propose a physics-aware generative adversarial network model, FluxGAN,
capable of simultaneously generating high-quality images of large
microstructures and description of their thermal properties. During the
training phase, the model learns about the relationship between the local
structural features and the physical processes, such as the heat flux in the
microstructures, due to external temperature gradients. Once trained, the model
generates new structural and associated heat flux environments, bypassing the
computationally expensive modeling. Our model provides a cost effective and
efficient approach over conventional modeling techniques, such as the finite
element method (FEM), for describing the thermal properties of microstructures.
The conventional approach requires computational modeling that scales with the
size of the microstructure model, therefore limiting the simulation to a given
size, resolution, and complexity of the model. In contrast, the FluxGAN model
uses synthesis-by-part approach and generates arbitrary large size images at
low computational cost. We demonstrate that the model can be utilized to
generate designs of thermal sprayed coatings that satisfies target thermal
properties. Furthermore, the model is capable of generating coating
microstructures and physical processes in three-dimensional (3D) domain after
being trained on two-dimensional (2D) examples. Our approach has the potential
to transform the design and optimization of thermal sprayed coatings for
various applications, including high-temperature and long-duration operation of
gas turbines for aircraft or ground-based power generators.