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We propose a physics-aware generative adversarial network model, FluxGAN,capable of simultaneously generating high-quality images of largemicrostructures and description of their thermal properties. During thetraining phase, the model learns about the relationship between the localstructural features and the physical processes, such as the heat flux in themicrostructures, due to external temperature gradients. Once trained, the modelgenerates new structural and associated heat flux environments, bypassing thecomputationally expensive modeling. Our model provides a cost effective andefficient approach over conventional modeling techniques, such as the finiteelement method (FEM), for describing the thermal properties of microstructures.The conventional approach requires computational modeling that scales with thesize of the microstructure model, therefore limiting the simulation to a givensize, resolution, and complexity of the model. In contrast, the FluxGAN modeluses synthesis-by-part approach and generates arbitrary large size images atlow computational cost. We demonstrate that the model can be utilized togenerate designs of thermal sprayed coatings that satisfies target thermalproperties. Furthermore, the model is capable of generating coatingmicrostructures and physical processes in three-dimensional (3D) domain afterbeing trained on two-dimensional (2D) examples. Our approach has the potentialto transform the design and optimization of thermal sprayed coatings forvarious applications, including high-temperature and long-duration operation ofgas turbines for aircraft or ground-based power generators.

FluxGAN: A Physics-Aware Generative Adversarial Network Model for Generating Microstructures That Maintain Target Heat Flux