AI-Driven Inverse Design Method for Metamaterials Achieves High Accuracy

The team from Huazhong University of Science and Technology has developed an AI-assisted inverse design method for metamaterials with an accuracy rate of 98.92%. This method, utilizing machine learning and finite element analysis, addresses complex issues in metamaterial design, such as parameter complexity and geometric deformation nonlinearity.

Metamaterials are materials with unique mechanical properties, widely used in seismic-resistant construction, aerospace, and biomedical fields. Traditional design methods require repeated trial and error, whereas inverse design optimizes the design process by starting from performance requirements, thereby shortening the development cycle.

The team leveraged AI technology to enhance design efficiency and precision through extensive data learning. By integrating inverse networks and forward networks, they achieved a direct mapping from performance to structure, quickly generating design solutions that meet specific performance requirements.

This research not only advances the field of metamaterial design but also provides new insights for mechanical intelligent systems. In the future, the team plans to explore more complex generative AI models to further enhance the richness and reliability of design solutions.