NMR Signal

NMR signal inversion aims to reconstruct underlying molecular information from measured NMR signals, a computationally intensive task crucial for various applications. Recent research focuses on improving inversion speed and accuracy using deep learning architectures, particularly convolutional neural networks, and comparing their performance against traditional regularization methods like Tikhonov and modified total generalized variation. These studies highlight the superior speed and often accuracy of deep learning, especially for complete datasets, while regularization techniques remain advantageous for severely undersampled data. This ongoing work promises faster and more robust analysis of NMR data, impacting fields ranging from materials science to biomedicine.