Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environment
This addresses thermal design challenges for engineering applications, though it appears incremental as it builds on existing generative and reinforcement learning methods.
The paper tackles thermal design optimization by developing a generative framework using multi-agent deep reinforcement learning with continuous geometric representation, achieving design space exploration without requiring shape derivatives or differentiable objective functions.
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.