Symbolic Network

Symbolic networks are computational models that learn and represent mathematical expressions directly from data, aiming to improve the interpretability of machine learning models and discover underlying physical laws. Current research focuses on developing novel architectures, such as differentiable symbolic networks trained via gradient descent and dynamically evolving networks guided by reinforcement learning, to overcome challenges in scalability and high-dimensional data. These advancements are improving the accuracy and efficiency of symbolic regression, with applications in areas like scientific discovery and the solution of partial differential equations, where interpretable models are crucial. The integration of physics-informed constraints further enhances the reliability and applicability of these networks in scientific modeling.