Dynamical Equation

Dynamical equations describe the evolution of systems over time, a fundamental problem across science and engineering. Current research focuses on developing methods to learn these equations from data, employing techniques like neural networks (including transformers and neural ODEs), and adapting them to handle noisy data, covariate shift, and high-dimensional systems. These advancements are improving the accuracy and efficiency of modeling complex systems, with applications ranging from optimizing energy systems to reconstructing biological processes and enabling more robust control of robotic systems.