{"total":25,"rows":[{"id":"2306.08827","title":"PINNacle: A Comprehensive Benchmark of Physics-Informed Neural Networks for Solving PDEs","created":"2023-06-15","cited_by_count":87,"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":"2210.00272","title":"FINDE: Neural Differential Equations for Finding and Preserving Invariant Quantities","created":"2022-10-01","cited_by_count":12,"area":null},{"id":"2212.00017","title":"Knowledge-augmented Deep Learning and Its Applications: A Survey","created":"2022-11-30","cited_by_count":10,"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":"2403.01371","title":"eXponential FAmily Dynamical Systems (XFADS): Large-scale nonlinear Gaussian state-space modeling","created":"2024-03-03","cited_by_count":7,"area":null},{"id":"2409.08439","title":"Input-to-State Stable Coupled Oscillator Networks for Closed-form Model-based Control in Latent Space","created":"2024-09-13","cited_by_count":7,"area":null},{"id":"2410.18868","title":"A Riemannian Framework for Learning Reduced-order Lagrangian Dynamics","created":"2024-10-24","cited_by_count":7,"area":null},{"id":"2303.17824","title":"Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets","created":"2023-03-31","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":"2112.01641","title":"Hamiltonian latent operators for content and motion disentanglement in image sequences","created":"2021-12-02","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":"2204.05077","title":"Learning Trajectories of Hamiltonian Systems with Neural Networks","created":"2022-04-11","cited_by_count":4,"area":null},{"id":"2308.11216","title":"Hamiltonian GAN","created":"2023-08-22","cited_by_count":4,"area":null},{"id":"2202.05737","title":"Improving Generalization via Uncertainty Driven Perturbations","created":"2022-02-11","cited_by_count":3,"area":null},{"id":"2212.10306","title":"Gaussian Process Latent Variable Modeling for Few-shot Time Series Forecasting","created":"2022-12-20","cited_by_count":3,"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":"2206.00447","title":"CD$^2$: Fine-grained 3D Mesh Reconstruction With Twice Chamfer Distance","created":"2022-06-01","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":"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":"2605.30812","title":"Learning Permutation-invariant Macroscopic Dynamics","created":"2026-05-29","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":"2211.09537","title":"Neural Langevin Dynamics: towards interpretable Neural Stochastic Differential Equations","created":"2022-11-17","cited_by_count":0,"area":null},{"id":"2403.08194","title":"Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework","created":"2024-03-13","cited_by_count":0,"area":null},{"id":"2605.23510","title":"Learning partially observed systems with neural Hamiltonian ordinary differential equations","created":"2026-05-22","cited_by_count":0,"area":null}],"next_cursor":null}