Dissipative Dynamic

Dissipative dynamics research focuses on modeling and understanding systems where energy is lost, contrasting with conservative systems. Current efforts leverage neural networks, particularly Neural Ordinary Differential Equations (NODEs) and variations like Recurrent Equilibrium Networks (RENs), often incorporating Lagrangian or Hamiltonian frameworks adapted to handle dissipation, sometimes through techniques like mirroring the system in a higher-dimensional space. This work aims to improve the accuracy and robustness of predictions for such systems, enabling better modeling of diverse phenomena from materials science to fluid dynamics. The resulting models offer improved system identification and stability analysis, with applications ranging from nonlinear system control to large-scale network analysis.