Metriplectic System

Metriplectic systems are mathematical frameworks that model dissipative systems while preserving fundamental thermodynamic principles like energy conservation and entropy increase. Current research focuses on developing neural network architectures, specifically port-metriplectic networks, to learn these systems from data, leveraging inductive biases to ensure thermodynamic consistency. This approach offers improved accuracy and scalability in modeling complex physical phenomena, enabling efficient prediction and analysis of systems where complete experimental characterization is challenging. The resulting models have significant potential for applications in diverse fields requiring accurate and efficient simulations of energy-dissipating processes.