Optimizing Bioinspired Composite Materials Using Deep Q‐Network Reinforcement Learning and Finite Element Methods

Bioinspired materials are often modeled after natural structures that have been optimized through evolutionary processes, resulting in exceptional properties such as increased strength, toughness, and resilience. However, the intricate geometries of these materials introduce significant computational challenges, particularly in the context of finite element analysis and structural design. To address these complexities, a novel design framework that integrates reinforcement learning with finite element methods is presented. Specifically, the Deep Q‐Network (DQN) algorithm is employed to optimize the design parameters of bionic composite materials. This approach allows for the precise tuning of material properties to meet specific design criteria while enabling real‐time performance evaluation during the design process. Compared to conventional methods, the framework offers a substantial reduction in design costs. Moreover, the results show that by strategically combining soft and stiff materials, composite structures can engineered with mechanical performance superior to those composed of a single material. This innovative method provides a highly efficient solution for designing bioinspired composite materials, effectively overcoming the computational challenges associated with their complex geometries.