{"total":17,"rows":[{"id":"2305.09017","title":"Gaussian Process Port-Hamiltonian Systems: Bayesian Learning with Physics Prior","created":"2023-05-15","cited_by_count":15,"area":null},{"id":"2102.01606","title":"Structure-preserving Gaussian Process Dynamics","created":"2021-02-02","cited_by_count":9,"area":null},{"id":"2211.16006","title":"Lie Group Forced Variational Integrator Networks for Learning and Control of Robot Systems","created":"2022-11-29","cited_by_count":7,"area":null},{"id":"2210.05087","title":"Approximation of nearly-periodic symplectic maps via structure-preserving neural networks","created":"2022-10-11","cited_by_count":6,"area":null},{"id":"2301.07928","title":"Hamiltonian Neural Networks with Automatic Symmetry Detection","created":"2023-01-19","cited_by_count":6,"area":null},{"id":"2303.01925","title":"Learning Energy Conserving Dynamics Efficiently with Hamiltonian Gaussian Processes","created":"2023-03-03","cited_by_count":6,"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":"2206.12272","title":"Physically Consistent Learning of Conservative Lagrangian Systems with Gaussian Processes","created":"2022-06-24","cited_by_count":5,"area":null},{"id":"2309.02351","title":"Exact Inference for Continuous-Time Gaussian Process Dynamics","created":"2023-09-05","cited_by_count":3,"area":null},{"id":"2404.07703","title":"Learning Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces and Random Features","created":"2024-04-11","cited_by_count":2,"area":null},{"id":"2406.18726","title":"Data-driven identification of port-Hamiltonian DAE systems by Gaussian processes","created":"2024-06-26","cited_by_count":2,"area":null},{"id":"2508.01068","title":"Learning the action for long-time-step simulations of molecular dynamics","created":"2025-08-01","cited_by_count":2,"area":null},{"id":"2509.07280","title":"Learning Generalized Hamiltonian Dynamics with Stability from Noisy Trajectory Data","created":"2025-09-08","cited_by_count":1,"area":null},{"id":"2511.05330","title":"Learning Dynamics from Input-Output Data with Hamiltonian Gaussian Processes","created":"2025-11-07","cited_by_count":1,"area":null},{"id":"2402.09018","title":"Neural Operators Meet Energy-based Theory: Operator Learning for Hamiltonian and Dissipative PDEs","created":"2024-02-14","cited_by_count":0,"area":null},{"id":"2605.06246","title":"Structure-Preserving Gaussian Processes Via Discrete Euler-Lagrange Equations","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2608.22324","title":"Gaussian process learning with flow map refinement for parameter estimation in dynamical systems","created":"2026-08-23","cited_by_count":0,"area":null}],"next_cursor":null}