  Generative design has been growing across the design community as a viable
method for design space exploration. Thermal design is more complex than
mechanical or aerodynamic design because of the additional convection-diffusion
equation and its pertinent boundary interaction. We present a generative
thermal design using cooperative multi-agent deep reinforcement learning and
continuous geometric representation of the fluid and solid domain. The proposed
framework consists of a pre-trained neural network surrogate model as an
environment to predict heat transfer and pressure drop of the generated
geometries. The design space is parameterized by composite Bezier curve to
solve multiple fin shape optimization. We show that our multi-agent framework
can learn the policy for design strategy using multi-objective reward without
the need for shape derivation or differentiable objective function.
