AwareLiquid-Physic

AwareLiquid-Physic is a machine learning model repository that integrates physics principles directly into its architecture, using a liquid substrate recurrence with a hard-constraint Hamiltonian head and symplectic integrator to ensure energy conservation in continuous-time trajectory prediction. It enables adaptable energy landscapes for different system trajectories, validated on physics benchmarks and suitable for physics-informed ML research and experimentation.