研究目的
To present a new approach for using artificial intelligence for knowledge discovery in electromagnetic nanostructures, focusing on assessing the feasibility of desired responses from nanophotonic structures.
研究成果
The study demonstrates a novel geometric deep learning-based approach for knowledge discovery in nanophotonics, capable of assessing the feasibility of desired responses from nanostructures with high accuracy. The method combines dimensionality reduction with convex-hull and one-class SVM algorithms, showing potential for facilitating the design and understanding of nanophotonic devices.
研究不足
The approach requires a significant amount of training data and computational resources for simulations. The accuracy of the method depends on the quality and quantity of the training data.
1：Experimental Design and Method Selection:
The methodology involves using full-wave EM simulations to generate training data for geometric deep learning algorithms. The approach combines dimensionality reduction (autoencoder) with convex-hull and one-class SVM algorithms to assess the feasibility of responses from nanophotonic structures.
2：Sample Selection and Data Sources:
Training data is obtained through simulations of randomly selected nanostructures. Validation is performed using another set of simulations.
3：List of Experimental Equipment and Materials:
The study uses COMSOL Multiphysics for simulations and Python for implementing the algorithms.
4：Experimental Procedures and Operational Workflow:
The process involves training the autoencoder, forming the convex-hull in the latent response space, and validating the approach with test data.
5：Data Analysis Methods:
The analysis includes assessing the accuracy of the approach in identifying feasible responses and the degree of feasibility using one-class SVM.
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