Variable Model

Variable model research focuses on identifying and utilizing relevant variables within complex systems, aiming to create more efficient and interpretable models. Current efforts concentrate on developing methods to discover hidden or latent variables from high-dimensional data, often employing neural networks, including variational autoencoders and graph neural networks, alongside techniques like canonical correlation analysis and physically-guided neural networks. This work is significant for improving model accuracy, reducing computational costs, and enhancing the explainability of complex systems across diverse fields, from industrial process monitoring to material science and causal inference.