Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environment

GANs generate new designs from an existing dataset utilizing a generator and a discriminator which are usually Deep Generative design has been growing across the Neural Networks (DNNs). The objective function of GANs design community as a viable method for design should be differentiable to utilize gradient-based optimization space exploration. Thermal design is more complex while reward of a deep RL can be defined based on the than mechanical or aerodynamic design because design requirements (Chen & Ahmed, 2021b). of the additional convection-diffusion equation and its pertinent boundary interaction. We Shape and Topology Optimization (TO) play a major role in present a generative thermal design using cooperative Generative models in engineering design (Chen & Ahmed, multi-agent deep reinforcement learning 2021a). Engineering design often require Finite Element and continuous geometric representation of the Analysis (FEA) or Computational Fluid Dynamics (CFD) fluid and solid domain. The proposed framework to assess the performance of the output design (Hoyer et al., consists of a pre-trained neural network surrogate 2019). These numerical approaches are computationally model as an environment to predict heat transfer expensive and require human expertise (Regenwetter et al., and pressure drop of the generated geometries.