{"total":41,"rows":[{"id":"2106.13008","title":"Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting","created":"2021-06-24","cited_by_count":1676,"area":null},{"id":"2402.10198","title":"SAMformer: Unlocking the Potential of Transformers in Time Series Forecasting with Sharpness-Aware Minimization and Channel-Wise Attention","created":"2024-02-15","cited_by_count":44,"area":null},{"id":"2106.11753","title":"Symplectic Learning for Hamiltonian Neural Networks","created":"2021-06-22","cited_by_count":35,"area":null},{"id":"2409.15771","title":"Zero-shot forecasting of chaotic systems","created":"2024-09-24","cited_by_count":28,"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":"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":"2206.07335","title":"On Numerical Integration in Neural Ordinary Differential Equations","created":"2022-06-15","cited_by_count":21,"area":null},{"id":"2505.11349","title":"Context parroting: A simple but tough-to-beat baseline for foundation models in scientific machine learning","created":"2025-05-16","cited_by_count":18,"area":null},{"id":"2109.09151","title":"Locally-symplectic neural networks for learning volume-preserving dynamics","created":"2021-09-19","cited_by_count":9,"area":null},{"id":"2111.15176","title":"Learning Large-Time-Step Molecular Dynamics with Graph Neural Networks","created":"2021-11-30","cited_by_count":9,"area":null},{"id":"2308.15349","title":"Lie-Poisson Neural Networks (LPNets): Data-Based Computing of Hamiltonian Systems with Symmetries","created":"2023-08-29","cited_by_count":9,"area":null},{"id":"2203.00128","title":"Learning Neural Hamiltonian Dynamics: A Methodological Overview","created":"2022-02-28","cited_by_count":8,"area":null},{"id":"2406.10327","title":"Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting","created":"2024-06-14","cited_by_count":8,"area":null},{"id":"2301.07928","title":"Hamiltonian Neural Networks with Automatic Symmetry Detection","created":"2023-01-19","cited_by_count":6,"area":null},{"id":"2303.17824","title":"Implementation and (Inverse Modified) Error Analysis for implicitly-templated ODE-nets","created":"2023-03-31","cited_by_count":6,"area":null},{"id":"2308.16331","title":"Symmetry Preservation in Hamiltonian Systems: Simulation and Learning","created":"2023-08-30","cited_by_count":6,"area":null},{"id":"2504.08277","title":"Enabling Automatic Differentiation with Mollified Graph Neural Operators","created":"2025-04-11","cited_by_count":6,"area":null},{"id":"2301.13516","title":"Recurrences reveal shared causal drivers of complex time series","created":"2023-01-31","cited_by_count":5,"area":null},{"id":"2106.10911","title":"Approximation capabilities of measure-preserving neural networks","created":"2021-06-21","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":"2408.09821","title":"Symplectic Neural Networks Based on Dynamical Systems","created":"2024-08-19","cited_by_count":4,"area":null},{"id":"2412.16787","title":"Symplectic Neural Flows for Modeling and Discovery","created":"2024-12-21","cited_by_count":4,"area":null},{"id":"2209.07646","title":"Bayesian Identification of Nonseparable Hamiltonian Systems Using Stochastic Dynamic Models","created":"2022-09-15","cited_by_count":3,"area":null},{"id":"2408.16160","title":"CLPNets: Coupled Lie-Poisson Neural Networks for Multi-Part Hamiltonian Systems with Symmetries","created":"2024-08-28","cited_by_count":3,"area":null},{"id":"2607.20002","title":"Post-Training in Time Series Foundation Models: A Unifying Framework","created":"2026-07-22","cited_by_count":3,"area":null},{"id":"2204.13843","title":"VPNets: Volume-preserving neural networks for learning source-free dynamics","created":"2022-04-29","cited_by_count":2,"area":null},{"id":"2310.20398","title":"A hybrid approach for solving the gravitational N-body problem with Artificial Neural Networks","created":"2023-10-31","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":"2509.23003","title":"Discovering Phase Space Structure in Learned Hamiltonian Systems","created":"2025-09-26","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":"2312.08944","title":"What's Next? Predicting Hamiltonian Dynamics from Discrete Observations of a Vector Field","created":"2023-12-14","cited_by_count":1,"area":null},{"id":"2507.14467","title":"Learning Stochastic Hamiltonian Systems via Stochastic Generating Function Neural Network","created":"2025-07-19","cited_by_count":1,"area":null},{"id":"2508.01854","title":"Moment Estimate and Variational Approach for Learning Generalized Diffusion with Non-gradient Structures","created":"2025-08-03","cited_by_count":1,"area":null},{"id":"2509.16026","title":"Time-adaptive SympNets for separable Hamiltonian systems","created":"2025-09-19","cited_by_count":1,"area":null},{"id":"2509.20212","title":"Time-adaptive H\\'enonNets for separable Hamiltonian systems","created":"2025-09-24","cited_by_count":1,"area":null},{"id":"2603.06354","title":"Frequency-Separable Hamiltonian Neural Network for Multi-Timescale Dynamics","created":"2026-03-06","cited_by_count":1,"area":null},{"id":"2410.15201","title":"Learning Nonholonomic Dynamics with Constraint Discovery","created":"2024-10-19","cited_by_count":0,"area":null},{"id":"2607.28939","title":"Latent Lie-Poisson Neural Networks (LLPNNs): Discovering the motion of Lie-Poisson systems through observable data and latent dynamics","created":"2026-07-31","cited_by_count":0,"area":null},{"id":"2607.28977","title":"Extrapolating the emergence of Hamiltonian chaos with random-feature Hamiltonian neural networks","created":"2026-07-31","cited_by_count":0,"area":null}],"next_cursor":null}