论文标题
通过边界表示和多代理合作环境的生成热设计
Generative Thermal Design Through Boundary Representation and Multi-Agent Cooperative Environment
论文作者
论文摘要
在整个设计社区中，生成设计一直在增长，作为设计空间探索的可行方法。由于具有附加的对流扩散方程及其相关边界相互作用，热设计比机械或空气动力学设计更为复杂。我们使用合作的多代理深钢筋学习以及流体和固体结构域的连续几何表示，提出了生成的热设计。所提出的框架由预训练的神经网络替代模型组成，作为预测生成几何形状的热传递和压降的环境。设计空间通过复合Bezier曲线进行参数化，以求解多个FIN形状优化。我们表明，我们的多代理框架可以使用多目标奖励来学习设计策略的策略，而无需形状推导或可区分的目标函数。
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.
