Diverse Equation

Diverse equation research focuses on automatically discovering and solving mathematical equations from data, aiming to improve scientific modeling and problem-solving across various disciplines. Current efforts leverage machine learning techniques, including physics-informed neural networks (PINNs), genetic algorithms, and large language models (LLMs), often combined with symbolic regression or Bayesian methods, to tackle challenges like equation discovery from limited or noisy data, high-dimensionality, and the identification of multiple solutions. This work has significant implications for automating scientific discovery, improving the efficiency of numerical simulations, and enabling more robust and accurate modeling of complex systems in fields ranging from physics and engineering to finance and ecology.