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AwareLiquid Physic
AwareLiquid Physic is a machine learning model for continuous-state trajectory prediction with integrated physics.
AwareLiquid Physic is a machine learning model that embeds physics computation directly into its architecture instead of using soft PDE-residual losses. A continuous-time liquid recurrence serves as the substrate, paired with a learned Hamiltonian advanced by a symplectic integrator as a hard architectural constraint. The model adapts its energy landscape per trajectory using a liquid context that conditions the Hamiltonian's potential. It targets continuous-state trajectory prediction and is validated on metrics like k-step rollout MSE and energy drift.
AwareLiquid Physic at a glance
Key features
- Liquid recurrence substrate with learned time constants
- Hard-constraint symplectic Hamiltonian head
- Unstructured control MLP field head
- Liquid Hamiltonian model coupling context and rollouts
- Zero-parameter Newtonian operator layer for benchmarking
- Parallel scan for O(log T) depth execution
Use it for
- Predicting continuous-state trajectories for physical systems
- Benchmarking hard-constraint models against unstructured control
- Evaluating long-horizon stability via energy drift and rollout MSE
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