{"total":30,"rows":[{"id":"2404.10024","title":"ClimODE: Climate and Weather Forecasting with Physics-informed Neural ODEs","created":"2024-04-15","cited_by_count":82,"area":null},{"id":"2308.13212","title":"SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases","created":"2023-08-25","cited_by_count":27,"area":null},{"id":"2305.15616","title":"Reversible and irreversible bracket-based dynamics for deep graph neural networks","created":"2023-05-24","cited_by_count":26,"area":null},{"id":"2209.11588","title":"Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network","created":"2022-09-23","cited_by_count":23,"area":null},{"id":"2211.05520","title":"Unravelling the Performance of Physics-informed Graph Neural Networks for Dynamical Systems","created":"2022-11-10","cited_by_count":23,"area":null},{"id":"2209.10740","title":"Enhancing the Inductive Biases of Graph Neural ODE for Modeling Dynamical Systems","created":"2022-09-22","cited_by_count":20,"area":null},{"id":"2306.09739","title":"Stabilized Neural Differential Equations for Learning Dynamics with Explicit Constraints","created":"2023-06-16","cited_by_count":15,"area":null},{"id":"2210.01407","title":"Homotopy-based training of NeuralODEs for accurate dynamics discovery","created":"2022-10-04","cited_by_count":14,"area":null},{"id":"2209.01476","title":"Learning the Dynamics of Particle-based Systems with Lagrangian Graph Neural Networks","created":"2022-09-03","cited_by_count":12,"area":null},{"id":"2211.01873","title":"Port-metriplectic neural networks: thermodynamics-informed machine learning of complex physical systems","created":"2022-11-03","cited_by_count":11,"area":null},{"id":"2207.12749","title":"Thermodynamics of learning physical phenomena","created":"2022-07-26","cited_by_count":10,"area":null},{"id":"2311.11362","title":"Symmetry-invariant quantum machine learning force fields","created":"2023-11-19","cited_by_count":8,"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":"2208.02632","title":"Unifying physical systems' inductive biases in neural ODE using dynamics constraints","created":"2022-08-03","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":"2505.20515","title":"Semi-Explicit Neural DAEs: Learning Long-Horizon Dynamical Systems with Algebraic Constraints","created":"2025-05-26","cited_by_count":4,"area":null},{"id":"2212.01168","title":"Towards Cross Domain Generalization of Hamiltonian Representation via Meta Learning","created":"2022-12-02","cited_by_count":3,"area":null},{"id":"2302.04400","title":"Discovering interpretable Lagrangian of dynamical systems from data","created":"2023-02-09","cited_by_count":3,"area":null},{"id":"2501.07373","title":"Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems","created":"2025-01-13","cited_by_count":3,"area":null},{"id":"2506.16079","title":"Investigating Lagrangian Neural Networks for Infinite Horizon Planning in Quadrupedal Locomotion","created":"2025-06-19","cited_by_count":3,"area":null},{"id":"2511.23307","title":"Hard-Constrained Neural Networks with Physics-Embedded Architecture for Residual Dynamics Learning and Invariant Enforcement in Cyber-Physical Systems","created":"2025-11-28","cited_by_count":3,"area":null},{"id":"2306.11435","title":"Graph Neural Stochastic Differential Equations for Learning Brownian Dynamics","created":"2023-06-20","cited_by_count":2,"area":null},{"id":"2307.05299","title":"Discovering Symbolic Laws Directly from Trajectories with Hamiltonian Graph Neural Networks","created":"2023-07-11","cited_by_count":2,"area":null},{"id":"2310.06241","title":"A Bayesian framework for discovering interpretable Lagrangian of dynamical systems from data","created":"2023-10-10","cited_by_count":2,"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":"2309.01069","title":"Separable Hamiltonian Neural Networks","created":"2023-09-03","cited_by_count":1,"area":null},{"id":"2410.11480","title":"Poisson-Dirac Neural Networks for Modeling Coupled Dynamical Systems across Domains","created":"2024-10-15","cited_by_count":1,"area":null},{"id":"2206.11030","title":"KeyCLD: Learning Constrained Lagrangian Dynamics in Keypoint Coordinates from Images","created":"2022-06-22","cited_by_count":0,"area":null},{"id":"2312.09775","title":"A Comparative Evaluation of Additive Separability Tests for Physics-Informed Machine Learning","created":"2023-12-15","cited_by_count":0,"area":null},{"id":"2505.13275","title":"Hamiltonian Neural PDE Solvers through Functional Approximation","created":"2025-05-19","cited_by_count":0,"area":null}],"next_cursor":null}