Data Driven Identification

Data-driven identification focuses on extracting meaningful information and models from data, bypassing traditional, often laborious, methods. Current research emphasizes developing robust and interpretable models, employing techniques like neural networks (including autoencoders and convolutional neural networks), Gaussian processes, and gradient boosting, to identify latent structures in diverse data types, ranging from physical systems to astronomical observations and medical images. This approach offers significant advantages in efficiency and scalability across various scientific disciplines and engineering applications, enabling faster analysis and potentially revealing hidden relationships previously inaccessible through conventional methods.