Spectral Model

Spectral modeling encompasses techniques that analyze data in the frequency domain to extract meaningful information and build predictive models. Current research focuses on improving the accuracy and reliability of these models, particularly within deep learning frameworks like convolutional neural networks (CNNs) and graph neural networks (GNNs), addressing challenges such as error characterization and operational range limitations through anomaly detection. This work is significant for diverse applications, including medical imaging analysis, exoplanet detection, and material science, enabling more robust and reliable inferences from complex datasets.