The field is witnessing a significant shift towards the development of efficient algorithms and the application of deep learning techniques to various problems. Researchers are focusing on improving the performance and scalability of existing methods, particularly in areas such as signal decomposition, orthogonalization, and inverse design. Notably, the use of local Fourier analysis, Transformer architectures, and Chebyshev-type polynomials is gaining traction. These innovative approaches are enabling the efficient computation of singular values, the decomposition of one-dimensional signals, and the optimization of orthogonal approximations. Furthermore, the integration of deep learning models, such as CNNs and LSTMs, is leading to improved accuracy and speed in inverse design and other applications. Some noteworthy papers in this area include: LFA applied to CNNs, which proposes an efficient method for computing singular values of convolutional mappings. Accelerating Newton-Schulz Iteration for Orthogonalization via Chebyshev-type Polynomials, which presents a Chebyshev-optimized version of the Newton-Schulz iteration for orthogonalization.