HGIN jointly recovers interaction graphs and predicts trajectories for lattice Hamiltonian systems from data, achieving six to thirteen orders of magnitude lower long-time errors than baselines on Klein-Gordon and discrete nonlinear Schrödinger lattices.
& Battaglia, P.Hamiltonian Graph Networks with ODE Integrators
4 Pith papers cite this work, alongside 76 external citations. Polarity classification is still indexing.
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NHODE framework learns partially observed dynamical systems by combining Hamiltonian neural networks with neural ODEs, enforcing energy conservation and improving long-horizon stability over data-driven baselines on mass-spring and three-body problems.
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.
citing papers explorer
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Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data
HGIN jointly recovers interaction graphs and predicts trajectories for lattice Hamiltonian systems from data, achieving six to thirteen orders of magnitude lower long-time errors than baselines on Klein-Gordon and discrete nonlinear Schrödinger lattices.
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Learning partially observed systems with neural Hamiltonian ordinary differential equations
NHODE framework learns partially observed dynamical systems by combining Hamiltonian neural networks with neural ODEs, enforcing energy conservation and improving long-horizon stability over data-driven baselines on mass-spring and three-body problems.
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Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges
Geometric deep learning provides a unified mathematical framework based on grids, groups, graphs, geodesics, and gauges to explain and extend neural network architectures by incorporating physical regularities.
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Graph Mamba Operator: A Latent Simulator for Interacting Particle Systems
GraMO couples graph interactions and temporal state updates in one linear recurrence with input-dependent coefficients to simulate N-body, motion, and robotics systems with lower long-horizon error than prior GNN or SSM approaches.