{"total":38,"rows":[{"id":"2107.01272","title":"Physics-Guided Deep Learning for Dynamical Systems: A Survey","created":"2021-07-02","cited_by_count":44,"area":null},{"id":"2109.06407","title":"Neural Networks with Physics-Informed Architectures and Constraints for Dynamical Systems Modeling","created":"2021-09-14","cited_by_count":41,"area":null},{"id":"2102.06794","title":"Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models","created":"2021-02-12","cited_by_count":34,"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":"2109.05364","title":"Structure-preserving Sparse Identification of Nonlinear Dynamics for Data-driven Modeling","created":"2021-09-11","cited_by_count":27,"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":"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":"2212.00893","title":"Compositional Learning of Dynamical System Models Using Port-Hamiltonian Neural Networks","created":"2022-12-01","cited_by_count":18,"area":null},{"id":"2212.02179","title":"Physics-Informed Model-Based Reinforcement Learning","created":"2022-12-05","cited_by_count":18,"area":null},{"id":"2201.05715","title":"Taylor-Lagrange Neural Ordinary Differential Equations: Toward Fast Training and Evaluation of Neural ODEs","created":"2022-01-14","cited_by_count":13,"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":"2306.06335","title":"How to Learn and Generalize From Three Minutes of Data: Physics-Constrained and Uncertainty-Aware Neural Stochastic Differential Equations","created":"2023-06-10","cited_by_count":10,"area":null},{"id":"2203.00128","title":"Learning Neural Hamiltonian Dynamics: A Methodological Overview","created":"2022-02-28","cited_by_count":8,"area":null},{"id":"2205.12550","title":"Recognition Models to Learn Dynamics from Partial Observations with Neural ODEs","created":"2022-05-25","cited_by_count":8,"area":null},{"id":"2204.06362","title":"A Review of Machine Learning Methods Applied to Structural Dynamics and Vibroacoustic","created":"2022-04-13","cited_by_count":7,"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":"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":"2311.15890","title":"Stability-Informed Initialization of Neural Ordinary Differential Equations","created":"2023-11-27","cited_by_count":5,"area":null},{"id":"2101.05537","title":"Optimal Energy Shaping via Neural Approximators","created":"2021-01-14","cited_by_count":4,"area":null},{"id":"2110.03266","title":"Lagrangian Neural Network with Differentiable Symmetries and Relational Inductive Bias","created":"2021-10-07","cited_by_count":4,"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":"2206.12325","title":"ModLaNets: Learning Generalisable Dynamics via Modularity and Physical Inductive Bias","created":"2022-06-24","cited_by_count":4,"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":"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":"2411.00110","title":"Lagrangian neural networks for nonholonomic mechanics","created":"2024-10-31","cited_by_count":2,"area":null},{"id":"2412.11215","title":"Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks","created":"2024-12-15","cited_by_count":2,"area":null},{"id":"2106.04166","title":"Incorporating NODE with Pre-trained Neural Differential Operator for Learning Dynamics","created":"2021-06-08","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":"2306.12077","title":"Learning Latent Dynamics via Invariant Decomposition and (Spatio-)Temporal Transformers","created":"2023-06-21","cited_by_count":1,"area":null},{"id":"2509.24517","title":"Performance-Carbon Trade-Offs across Architectural Biases in Shear Flow Forecasting","created":"2025-09-29","cited_by_count":0,"area":null},{"id":"2510.00401","title":"Physics-Informed Neural Controlled Differential Equations for Scalable Long Horizon Multi-Agent Motion Forecasting","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2603.20474","title":"From Data to Laws: Neural Discovery of Conservation Laws Without False Positives","created":"2026-03-20","cited_by_count":0,"area":null}],"next_cursor":null}