Liquid Neural Network

Liquid neural networks (LNNs) are a class of neural networks inspired by biological neural systems, aiming to create adaptable and efficient models for various tasks. Current research focuses on enhancing LNN architectures, such as Liquid Time-Constant (LTC) networks and their variants, to improve performance in applications like real-time simulations, sim-to-real transfer learning in robotics, and adaptive learning in dynamic environments. These networks show promise in addressing challenges related to concept drift and computational cost, particularly in fields like healthcare (digital twins) and scientific computing, offering faster and more robust solutions compared to traditional methods.