Here we utilize a low-rank tensor model (LTM) as a function approximator, combined with the gradient descent method, to solve eigenvalue problems including the Laplacian operator and the harmonic oscillator. Experimental results show the superiority of the polynomial-based low-rank tensor model (PLTM) compared to the tensor neural network (TNN). We also test such low-rank architectures for the classification problem on the MNIST dataset.

翻译：在这里,我们使用一个低级高压模型(LTM)作为功能近似器(LTM),加上梯度下降法,以解决包括拉普拉西亚操作员和声波振荡器在内的电子价值问题。实验结果显示,与超时神经网络(TNN)相比,多级低级高压模型(PLTM)优于超音速神经网络(TNN ) 。我们还在MNIST数据集中测试这种低级结构来处理分类问题。