Paper Citation Record · LEDGER

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach

As of 19 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 0 inbound Pith citation observations for arXiv:2509.24627.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.24627 v2

Coverage vector

measured 49 of 49 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T13:54:17.897051Z

measured 49 of 49 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

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Source: cited_works

Reference resolution

49 of 49 outbound references displayed

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External citation measurements

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Outbound references

Observation ada11a83-f4fb-4dfb-b19d-a15d03286f83 · outbound

This paper cites an unresolved cited work.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Unresolved cited work

Reference 1

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This paper cites Optimization Algorithms on Matrix Manifolds.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Optimization Algorithms on Matrix Manifolds

Reference 2

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Observation c97d8c98-4111-452a-ac12-37533b365639 · outbound

This paper cites Riemannian adaptive optimization methods.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Riemannian adaptive optimization methods

Reference 3

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This paper cites Geometric optimization for structure-preserving model reduction of hamiltonian systems.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Geometric optimization for structure-preserving model reduction of hamiltonian systems

Reference 4

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Observation 591b13cf-69c7-4738-ba58-2c6fb07c988d · outbound

This paper cites Which priors matter? B enchmarking models for learning latent dynamics.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Which priors matter? B enchmarking models for learning latent dynamics

Reference 5

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This paper cites An introduction to optimization on smooth manifolds.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach An introduction to optimization on smooth manifolds

Reference 6

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This paper cites Brunton, Joshua L.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Brunton, Joshua L

Reference 7

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This paper cites Symplectic model reduction of H amiltonian systems on nonlinear manifolds and approximation with weakly symplectic autoencoder.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Symplectic model reduction of H amiltonian systems on nonlinear manifolds and approximation with weakly symplectic autoencoder

Reference 8

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Observation db1e5f57-6cbd-4944-840f-e964ff80fabf · outbound

This paper cites Model reduction on manifolds: A differential geometric framework.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Model reduction on manifolds: A differential geometric framework

Reference 9

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This paper cites Nathan Kutz, and Steven L.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Nathan Kutz, and Steven L

Reference 10

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This paper cites Neural symplectic form: Learning H amiltonian equations on general coordinate systems.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Neural symplectic form: Learning H amiltonian equations on general coordinate systems

Reference 11

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This paper cites Symplectic recurrent neural networks.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Symplectic recurrent neural networks

Reference 12

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Observation df8b6b89-46c5-49ea-9211-56e8cd5b1e49 · outbound

This paper cites Lagrangian Neural Networks.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Lagrangian Neural Networks

Reference 13

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This paper cites Fernandes, and Waldyr M.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Fernandes, and Waldyr M

Reference 14

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This paper cites H amiltonian -based neural ODE networks on the SE (3) manifold for dynamics learning and control.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach H amiltonian -based neural ODE networks on the SE (3) manifold for dynamics learning and control

Reference 15

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This paper cites Geometries and interpolations for symmetric positive definite matrices, pp.\ 85--113.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Geometries and interpolations for symmetric positive definite matrices, pp.\ 85--113

Reference 16

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Observation e3393a7a-6bb0-405d-b50f-aa616a05fb08 · outbound

This paper cites A R iemannian framework for learning reduced-order L agrangian dynamics.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach A R iemannian framework for learning reduced-order L agrangian dynamics

Reference 17

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Observation 3e407039-1b27-4f77-a1e1-1ecb3079c67d · outbound

This paper cites H amiltonian neural networks.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach H amiltonian neural networks

Reference 18

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This paper cites Hamilton.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Hamilton

Reference 19

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This paper cites Sympnets: Intrinsic structure-preserving symplectic networks for identifying H amiltonian systems.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Sympnets: Intrinsic structure-preserving symplectic networks for identifying H amiltonian systems

Reference 20

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This paper cites Riemann tensor neural networks: Learning conservative systems with physics-constrained networks.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Riemann tensor neural networks: Learning conservative systems with physics-constrained networks

Reference 21

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This paper cites Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Kevrekidis, Lu Lu, Paris Perdikaris, Sifan Wang, and Liu Yang

Reference 22

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Geoopt: Riemannian Optimization in PyTorch

Reference 23

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Unresolved cited work

Reference 24

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Simulating Hamiltonian Dynamics

Reference 25

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This paper cites Neural autoencoder-based structure-preserving model order reduction and control design for high-dimensional physical systems.

Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Neural autoencoder-based structure-preserving model order reduction and control design for high-dimensional physical systems

Reference 26

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Harnessing the power of neural operators with automatically encoded conservation laws

Reference 27

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Combining physics and deep learning to learn continuous-time dynamics models

Reference 28

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Otto, Gregory R

Reference 29

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Symplectic model reduction of H amiltonian systems

Reference 30

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach A R iemannian framework for tensor computing

Reference 31

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Hamiltonian fluid mechanics

Reference 32

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Unresolved cited work

Reference 33

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Quantisierung als eigenwertproblem

Reference 34

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Preserving lagrangian structure in data-driven reduced-order modeling of large-scale dynamical systems

Reference 35

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Symplectic model reduction of H amiltonian systems using data-driven quadratic manifolds

Reference 36

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Najera-Flores, Michael D

Reference 37

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

Reference 38

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Explicit symplectic approximation of nonseparable H amiltonians: Algorithm and long time performance

Reference 39

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Mujoco: A physics engine for model-based control

Reference 40

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Understanding and mitigating gradient flow pathologies in physics-informed neural networks

Reference 41

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Nonseparable symplectic neural networks

Reference 42

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Dissipative SymODEN : Encoding H amiltonian dynamics with dissipation and control into deep learning

Reference 43

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Symplectic ODE -net: Learning H amiltonian dynamics with control

Reference 44

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Extending L agrangian and H amiltonian neural networks with differentiable contact models

Reference 45

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach write newline

Reference 46

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach @esa (Ref

Reference 47

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach Unresolved cited work

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Observation d5509a83-9f72-4b12-b6a9-d944e5ca1f95 · outbound

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Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach ^iziK W;x^.^ m mx xi _ ׵^Sq× = ޟik_K; ڞ I||]˚h緯u ,gx^ضڃ0ل 4s ! q^`K )Okْ

Reference 49

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