Innovative Electromagnetic Research

Combining theory and experiments to enhance metamaterial design and validation for improved computational efficiency.

Electromagnetic Research

Analyzing metamaterials through theoretical and experimental validation methods.

A transparent, reflective object shaped like the letter 'M' is placed on a metallic, grid-like surface. The object reflects a spectrum of colors, creating a prismatic effect, and appears three-dimensional against the geometric pattern of the background.

Model Validation

Comparative experiments assess computational efficiency and accuracy.

A geometric pattern of metallic grid structures with triangular openings, featuring a high-contrast black and white color scheme and dramatic shadows.

Data Analysis

Utilizing public datasets for model performance evaluation.

A close-up view of an intricately woven metallic mesh pattern with a repetitive, almost geometric design. The strands of metal are interlaced, creating a three-dimensional effect with shadows and highlights. The background is a gradient of dark to light gray, enhancing the depth of the pattern.

A close-up view of a perforated metal surface with blue light shining through the small circular holes, creating an abstract and futuristic appearance.

Inverse Solving

New design framework for solving electromagnetic response models.

Experimental Design

Framework supports theoretical analysis and experimental validation processes.

The image features a complex geometric structure composed of crisscrossing metal beams and a perforated metal facade. The design elements create a sense of depth and intricate detail, with light partially filtering through the spaces in between.

The expected outcomes of this research include: 1) Proposing an AI-based design framework for inverse solving models of electromagnetic responses in metamaterials, providing innovative solutions for metamaterial design; 2) Validating the advantages of this model in enhancing the efficiency and accuracy of inverse solving, offering a basis for practical applications; 3) Identifying key technical bottlenecks in the application of AI in electromagnetics and proposing optimization strategies, promoting further development in related fields. These outcomes will help improve the efficiency and accuracy of metamaterial design, advance the deep integration of AI in electromagnetics, and provide experimental data and application scenarios for the further optimization of OpenAI models.