Spectral Domain

Spectral domain analysis focuses on representing and processing data using its frequency components, offering advantages in various applications by leveraging the inherent structure in frequency space. Current research emphasizes developing efficient algorithms and neural network architectures, such as spectral-based PINNs and transformer-based models, to improve accuracy and speed in solving partial differential equations, classifying time series, and processing signals in diverse domains (audio, images, point clouds). These advancements are impacting fields ranging from neuroscience and medical imaging to sound event localization and material analysis, enabling more robust and efficient solutions to complex problems. The interpretability of spectral methods is also a growing focus, particularly in applications where understanding the underlying processes is crucial.