{"total":23,"rows":[{"id":"2106.13898","title":"Closed-form Continuous-time Neural Models","created":"2021-06-25","cited_by_count":65,"area":null},{"id":"2111.01495","title":"Constructing Neural Network-Based Models for Simulating Dynamical Systems","created":"2021-11-02","cited_by_count":31,"area":null},{"id":"2502.09885","title":"Comprehensive Review of Neural Differential Equations for Time Series Analysis","created":"2025-02-14","cited_by_count":20,"area":null},{"id":"2203.11805","title":"Robust Classification using Contractive Hamiltonian Neural ODEs","created":"2022-03-22","cited_by_count":13,"area":null},{"id":"2502.02480","title":"Stable Port-Hamiltonian Neural Networks","created":"2025-02-04","cited_by_count":13,"area":null},{"id":"2304.12707","title":"Lyapunov-Stable Deep Equilibrium Models","created":"2023-04-25","cited_by_count":10,"area":null},{"id":"2105.14070","title":"Accelerating Neural ODEs Using Model Order Reduction","created":"2021-05-28","cited_by_count":9,"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":"2304.02976","title":"Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations","created":"2023-04-06","cited_by_count":8,"area":null},{"id":"2408.08055","title":"DeNOTS: Stable Deep Neural ODEs for Time Series","created":"2024-08-15","cited_by_count":5,"area":null},{"id":"2411.02292","title":"ControlSynth Neural ODEs: Modeling Dynamical Systems with Guaranteed Convergence","created":"2024-11-04","cited_by_count":5,"area":null},{"id":"2412.01181","title":"Training Stiff Neural Ordinary Differential Equations with Explicit Exponential Integration Methods","created":"2024-12-02","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":"2401.04979","title":"DualDynamics: Synergizing Implicit and Explicit Methods for Robust Irregular Time Series Analysis","created":"2024-01-10","cited_by_count":4,"area":null},{"id":"2106.03780","title":"Learning Stochastic Optimal Policies via Gradient Descent","created":"2021-06-07","cited_by_count":3,"area":null},{"id":"2508.01519","title":"The Vanishing Gradient Problem for Stiff Neural Differential Equations","created":"2025-08-02","cited_by_count":3,"area":null},{"id":"2510.09685","title":"Deep Neural Networks Inspired by Differential Equations","created":"2025-10-09","cited_by_count":2,"area":null},{"id":"2411.13914","title":"ICODE: Modeling Dynamical Systems with Extrinsic Input Information","created":"2024-11-21","cited_by_count":1,"area":null},{"id":"2604.00277","title":"Hybrid Energy-Based Models for Physical AI: Provably Stable Identification of Port-Hamiltonian Dynamics","created":"2026-03-31","cited_by_count":1,"area":null},{"id":"2212.13033","title":"Modeling Nonlinear Dynamics in Continuous Time with Inductive Biases on Decay Rates and/or Frequencies","created":"2022-12-26","cited_by_count":0,"area":null},{"id":"2307.04772","title":"Digital Twins for Patient Care via Knowledge Graphs and Closed-Form Continuous-Time Liquid Neural Networks","created":"2023-07-08","cited_by_count":0,"area":null},{"id":"2507.10132","title":"Wavelet-Enhanced Neural ODE and Graph Attention for Interpretable Energy Forecasting","created":"2025-07-14","cited_by_count":0,"area":null},{"id":"2508.11432","title":"Robust Convolution Neural ODEs via Contractivity-promoting regularization","created":"2025-08-15","cited_by_count":0,"area":null}],"next_cursor":null}