Accurate spectrum prediction in design of terahertz (THz) devices remains challenging, especially for THz fibers. In this article, we propose an approach based on artificial intelligence (AI) assisted finite element method (FEM) to achieve accurate spectrum prediction for the design of THz fiber operating in 6G communication window. The antiresonant THz fiber has been selected to verify the effectiveness of this method. Initially, the principle and physical modeling of antiresonant THz fiber are analyzed. The spectra of THz fibers with different structural parameters are designed and predicted by FEM simulation. Then, the THz fibers are fabricated by 3D printing technology and the spectra are measured by a THz time domain spectroscopy system. The spectral dataset obtained by both forms are prepared for the modeling of AI-assisted FEM methods. Different AI algorithms are induced in FEM to predict experimental spectrum, including elman neural network (Elman), support vector machines (SVM), and general regression neutral network (GRNN). The prediction performance obtained by different methods are compared and analyzed comprehensively to confirm the effectiveness of proposed methods. The AI-assisted FEM methods show great improvement of prediction accuracy in the design of THz fibers. It provides accurate data support for THz device modeling assisted by machine learning.

In this paper, a new self-adaptive algorithm with the inertial technique is proposed for solving the split equality problem in $ p $-uniformly convex and uniformly smooth Banach spaces. Under some mild control conditions, a strong convergence theorem for the proposed algorithm is established. Furthermore, the results are applied to split equality fixed point problem and split equality variational inclusion problem. Finally, numerical examples are provided to illustrate the convergence behaviour of the algorithm. The main results in this paper improve and generalize some existing results in the literature.

Hollow-core terahertz (THz) fibers have attracted a lot of research interest due to the low loss and easy inner modification with functional materials. Liquid water has unique properties in the THz region and has been widely investigated in THz emission, sensing, and devices. In this paper, a hollow-core THz Bragg fiber with a water defect layer is proposed. The finite element method is used to verify and analyze the tunable temperature characteristic of the water-filled THz fiber. The numerical analysis results show that the confinement loss and the low-frequency side of the dip near 0.5 THz can be controlled by the temperature of the liquid water. The temperature sensitivity of the THz fiber is obtained at 0.09614 dB·m⁻¹/K at 0.45 THz with a high core power fraction up to 98%. The proposed THz fiber has potential applications in THz interaction with liquid and THz tunable devices.