{"total":31,"rows":[{"id":"2103.05632","title":"Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps","created":"2021-03-09","cited_by_count":41,"area":null},{"id":"2106.11753","title":"Symplectic Learning for Hamiltonian Neural Networks","created":"2021-06-22","cited_by_count":35,"area":null},{"id":"2202.04836","title":"Deconstructing the Inductive Biases of Hamiltonian Neural Networks","created":"2022-02-10","cited_by_count":31,"area":null},{"id":"2111.05458","title":"Which priors matter? Benchmarking models for learning latent dynamics","created":"2021-11-09","cited_by_count":26,"area":null},{"id":"2109.09151","title":"Locally-symplectic neural networks for learning volume-preserving dynamics","created":"2021-09-19","cited_by_count":9,"area":null},{"id":"2111.05986","title":"SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision","created":"2021-11-10","cited_by_count":5,"area":null},{"id":"2305.01338","title":"Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models","created":"2023-05-02","cited_by_count":5,"area":null},{"id":"2204.05077","title":"Learning Trajectories of Hamiltonian Systems with Neural Networks","created":"2022-04-11","cited_by_count":4,"area":null},{"id":"2408.09821","title":"Symplectic Neural Networks Based on Dynamical Systems","created":"2024-08-19","cited_by_count":4,"area":null},{"id":"2412.16787","title":"Symplectic Neural Flows for Modeling and Discovery","created":"2024-12-21","cited_by_count":4,"area":null},{"id":"2209.07646","title":"Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models","created":"2022-09-15","cited_by_count":3,"area":null},{"id":"2408.16160","title":"CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries","created":"2024-08-28","cited_by_count":3,"area":null},{"id":"2102.11923","title":"KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss","created":"2021-02-22","cited_by_count":2,"area":null},{"id":"2204.13843","title":"VPNets: Volume-preserving neural networks for learning source-free dynamics","created":"2022-04-29","cited_by_count":2,"area":null},{"id":"2401.12476","title":"Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling","created":"2024-01-23","cited_by_count":2,"area":null},{"id":"2409.11138","title":"Learning Generalized Hamiltonians using fully Symplectic Mappings","created":"2024-09-17","cited_by_count":2,"area":null},{"id":"2411.17511","title":"Training Hamiltonian neural networks without backpropagation","created":"2024-11-26","cited_by_count":2,"area":null},{"id":"2506.06558","title":"Rapid training of Hamiltonian graph networks using random features","created":"2025-06-06","cited_by_count":2,"area":null},{"id":"2509.24627","title":"Learning Hamiltonian Dynamics at Scale: A Differential-Geometric Approach","created":"2025-09-29","cited_by_count":2,"area":null},{"id":"2209.01521","title":"Symplectically Integrated Symbolic Regression of Hamiltonian Dynamical Systems","created":"2022-09-04","cited_by_count":1,"area":null},{"id":"2210.00090","title":"Data-driven discovery of non-Newtonian astronomy via learning non-Euclidean Hamiltonian","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2506.18812","title":"Learning Physical Systems: Symplectification via Gauge Fixing in Dirac Structures","created":"2025-06-23","cited_by_count":1,"area":null},{"id":"2507.14467","title":"Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network","created":"2025-07-19","cited_by_count":1,"area":null},{"id":"2509.13425","title":"Unified Spatiotemporal Physics-Informed Learning (USPIL): A Framework for Modeling Complex Predator-Prey Dynamics","created":"2025-09-16","cited_by_count":1,"area":null},{"id":"2509.16026","title":"Time-adaptive SympNets for separable Hamiltonian systems","created":"2025-09-19","cited_by_count":1,"area":null},{"id":"2509.20212","title":"Time-adaptive H\\'enonNets for separable Hamiltonian systems","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2602.15704","title":"Controlled oscillation modeling using port-Hamiltonian neural networks","created":"2026-02-17","cited_by_count":1,"area":null},{"id":"2604.17470","title":"Machine Learning Hamiltonian Dynamical Systems with Sparse and Noisy Data","created":"2026-04-19","cited_by_count":1,"area":null},{"id":"2406.16956","title":"Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints","created":"2024-06-20","cited_by_count":0,"area":null},{"id":"2604.23606","title":"Hamiltonian Graph Inference Networks: Joint structure discovery and dynamics prediction for lattice Hamiltonian systems from trajectory data","created":"2026-04-26","cited_by_count":0,"area":null},{"id":"2607.20235","title":"Dynamical and Optimization Trade-offs of Levi--Civita Coordinates for Learned Close-Encounter Dynamics","created":"2026-07-22","cited_by_count":0,"area":null}],"next_cursor":null}