Variational Model

Variational models are mathematical frameworks used to approximate complex probability distributions, primarily aiming to solve inverse problems and learn efficient representations from data. Current research focuses on improving the accuracy and efficiency of these models, often employing deep learning architectures like variational autoencoders (VAEs) and recurrent neural networks, alongside novel algorithms such as unrolling methods and bilevel optimization. These advancements are impacting diverse fields, including medical imaging (e.g., CT reconstruction, image segmentation), robotics (e.g., human-robot interaction), and physics (e.g., simulating phase transitions), by enabling more accurate and efficient solutions to challenging problems with limited data.