HAAD detects deepfakes by modeling latent manifolds as potential energy surfaces and quantifying instability via Hamiltonian trajectory statistics such as action and energy dissipation.
Lagrangian neural networks.arXiv:2003.04630
7 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
representative citing papers
piDMD learns a single parameter-affine Koopman surrogate ROM from training samples at multiple parameters to predict dynamics at unseen parameters with improved robustness over interpolation baselines.
LaWM induces latent transitions from a learned discrete variational principle rather than an unconstrained neural predictor, yielding improved physical consistency on synthetic dynamics and robot benchmarks.
Hamiltonian World Models structure latent dynamics around energy-conserving Hamiltonian evolution to produce physically grounded, action-controllable predictions for embodied decision making.
Mesh Field Theory reduces mesh-based physics to port-Hamiltonian form with topology fixing interconnections and metrics entering only via constitutive relations, enabling MeshFT-Net to achieve near-zero energy drift, correct dispersion, momentum conservation, and strong out-of-distribution fidelity.
DiLaR-PINN learns dissipative effects in electromechanical systems via a skew-dissipative latent residual PINN that guarantees non-increasing energy and uses recurrent curriculum training for partial observations.
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.
citing papers explorer
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Detecting Deepfakes via Hamiltonian Dynamics
HAAD detects deepfakes by modeling latent manifolds as potential energy surfaces and quantifying instability via Hamiltonian trajectory statistics such as action and energy dissipation.
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Parametric Interpolation of Dynamic Mode Decomposition for Predicting Nonlinear Systems
piDMD learns a single parameter-affine Koopman surrogate ROM from training samples at multiple parameters to predict dynamics at unseen parameters with improved robustness over interpolation baselines.
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LaWM: Least Action World Models for Long-Horizon Physical Consistency from Visual Observations
LaWM induces latent transitions from a learned discrete variational principle rather than an unconstrained neural predictor, yielding improved physical consistency on synthetic dynamics and robot benchmarks.
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Physically Native World Models: A Hamiltonian Perspective on Generative World Modeling
Hamiltonian World Models structure latent dynamics around energy-conserving Hamiltonian evolution to produce physically grounded, action-controllable predictions for embodied decision making.
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Mesh Field Theory: Port-Hamiltonian Formulation of Mesh-Based Physics
Mesh Field Theory reduces mesh-based physics to port-Hamiltonian form with topology fixing interconnections and metrics entering only via constitutive relations, enabling MeshFT-Net to achieve near-zero energy drift, correct dispersion, momentum conservation, and strong out-of-distribution fidelity.
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Dissipative Latent Residual Physics-Informed Neural Networks for Modeling and Identification of Electromechanical Systems
DiLaR-PINN learns dissipative effects in electromechanical systems via a skew-dissipative latent residual PINN that guarantees non-increasing energy and uses recurrent curriculum training for partial observations.
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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.