{"total":148,"rows":[{"id":"2109.13459","title":"Multiwavelet-based Operator Learning for Differential Equations","created":"2021-09-28","cited_by_count":234,"area":null},{"id":"2204.11127","title":"U-NO: U-shaped Neural Operators","created":"2022-04-23","cited_by_count":230,"area":null},{"id":"2104.02646","title":"gradSim: Differentiable simulation for system identification and visuomotor control","created":"2021-04-06","cited_by_count":100,"area":null},{"id":"2212.07564","title":"AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutions","created":"2022-12-15","cited_by_count":82,"area":null},{"id":"2103.05487","title":"UnICORNN: A recurrent model for learning very long time dependencies","created":"2021-03-09","cited_by_count":56,"area":null},{"id":"2106.12619","title":"Machine learning structure preserving brackets for forecasting irreversible processes","created":"2021-06-23","cited_by_count":52,"area":null},{"id":"2202.01889","title":"Generalizing to New Physical Systems via Context-Informed Dynamics Model","created":"2022-02-01","cited_by_count":50,"area":null},{"id":"2201.10085","title":"Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately","created":"2022-01-25","cited_by_count":47,"area":null},{"id":"2309.12252","title":"Parallelizing non-linear sequential models over the sequence length","created":"2023-09-21","cited_by_count":45,"area":null},{"id":"2102.04877","title":"Noisy Recurrent Neural Networks","created":"2021-02-09","cited_by_count":42,"area":null},{"id":"2103.05632","title":"Data-driven Prediction of General Hamiltonian Dynamics via Learning Exactly-Symplectic Maps","created":"2021-03-09","cited_by_count":41,"area":null},{"id":"2110.04744","title":"Long Expressive Memory for Sequence Modeling","created":"2021-10-10","cited_by_count":41,"area":null},{"id":"2109.07719","title":"Efficient Differentiable Simulation of Articulated Bodies","created":"2021-09-16","cited_by_count":39,"area":null},{"id":"2106.12782","title":"Hamiltonian-based Neural ODE Networks on the SE(3) Manifold For Dynamics Learning and Control","created":"2021-06-24","cited_by_count":38,"area":null},{"id":"2207.02542","title":"Tractable Dendritic RNNs for Reconstructing Nonlinear Dynamical Systems","created":"2022-07-06","cited_by_count":36,"area":null},{"id":"2106.11753","title":"Symplectic Learning for Hamiltonian Neural Networks","created":"2021-06-22","cited_by_count":35,"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":"2203.01874","title":"Thermodynamics-informed graph neural networks","created":"2022-03-03","cited_by_count":33,"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":"2307.05432","title":"Self-Supervised Learning with Lie Symmetries for Partial Differential Equations","created":"2023-07-11","cited_by_count":29,"area":null},{"id":"2202.08494","title":"Learning continuous models for continuous physics","created":"2022-02-17","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":"2112.09161","title":"Constraint-based graph network simulator","created":"2021-12-16","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":"2108.07799","title":"An Extensible Benchmark Suite for Learning to Simulate Physical Systems","created":"2021-08-09","cited_by_count":24,"area":null},{"id":"2211.07377","title":"Physics-Guided, Physics-Informed, and Physics-Encoded Neural Networks in Scientific Computing","created":"2022-11-14","cited_by_count":24,"area":null},{"id":"2310.06396","title":"Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach","created":"2023-10-10","cited_by_count":24,"area":null},{"id":"2102.13235","title":"Adaptable Hamiltonian neural networks","created":"2021-02-25","cited_by_count":21,"area":null},{"id":"2110.01894","title":"Combining Physics and Deep Learning to learn Continuous-Time Dynamics Models","created":"2021-10-05","cited_by_count":20,"area":null},{"id":"2304.07599","title":"Learning in latent spaces improves the predictive accuracy of deep neural operators","created":"2023-04-15","cited_by_count":19,"area":null},{"id":"2405.16305","title":"Efficiently Parameterized Neural Metriplectic Systems","created":"2024-05-25","cited_by_count":19,"area":null},{"id":"2206.02660","title":"Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces","created":"2022-06-06","cited_by_count":18,"area":null},{"id":"2101.05186","title":"MC-LSTM: Mass-Conserving LSTM","created":"2021-01-13","cited_by_count":17,"area":null},{"id":"2209.11661","title":"Exact conservation laws for neural network integrators of dynamical systems","created":"2022-09-23","cited_by_count":15,"area":null},{"id":"2403.12335","title":"Temporally Consistent Koopman Autoencoders for Forecasting Dynamical Systems","created":"2024-03-19","cited_by_count":15,"area":null},{"id":"2201.13254","title":"Learning Hamiltonians of constrained mechanical systems","created":"2022-01-31","cited_by_count":14,"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":"2102.09750","title":"Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory","created":"2021-02-19","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":"2208.10387","title":"Constants of motion network","created":"2022-08-22","cited_by_count":10,"area":null},{"id":"2210.02373","title":"Dynamical systems' based neural networks","created":"2022-10-05","cited_by_count":10,"area":null},{"id":"2304.14374","title":"Pseudo-Hamiltonian neural networks for learning partial differential equations","created":"2023-04-27","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":"2401.03728","title":"Generalized Lagrangian Neural Networks","created":"2024-01-08","cited_by_count":10,"area":null},{"id":"2102.01606","title":"Structure-preserving Gaussian Process Dynamics","created":"2021-02-02","cited_by_count":9,"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":"2208.03680","title":"On Fast Simulation of Dynamical System with Neural Vector Enhanced Numerical Solver","created":"2022-08-07","cited_by_count":9,"area":null},{"id":"2210.13414","title":"Thermodynamics-informed neural networks for physically realistic mixed reality","created":"2022-10-24","cited_by_count":9,"area":null},{"id":"2308.09939","title":"Understanding Self-attention Mechanism via Dynamical System Perspective","created":"2023-08-19","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":"2102.11544","title":"Identifying Physical Law of Hamiltonian Systems via Meta-Learning","created":"2021-02-23","cited_by_count":8,"area":null},{"id":"2106.03589","title":"Nonparametric adaptive control and prediction: theory and randomized algorithms","created":"2021-06-07","cited_by_count":8,"area":null},{"id":"2203.00128","title":"Learning Neural Hamiltonian Dynamics: A Methodological Overview","created":"2022-02-28","cited_by_count":8,"area":null},{"id":"2508.06981","title":"Structure-Preserving Digital Twins via Conditional Neural Whitney Forms","created":"2025-08-09","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":"2308.14666","title":"Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution","created":"2023-08-24","cited_by_count":7,"area":null},{"id":"2310.20425","title":"Discussing the Spectrum of Physics-Enhanced Machine Learning; a Survey on Structural Mechanics Applications","created":"2023-10-31","cited_by_count":7,"area":null},{"id":"2506.05259","title":"Learning long range dependencies through time reversal symmetry breaking","created":"2025-06-05","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":"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":"2304.12865","title":"Constraining Chaos: Enforcing dynamical invariants in the training of recurrent neural networks","created":"2023-04-24","cited_by_count":6,"area":null},{"id":"2308.01084","title":"Data-Driven Identification of Quadratic Representations for Nonlinear Hamiltonian Systems using Weakly Symplectic Liftings","created":"2023-08-02","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":"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":"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":"2211.10830","title":"Discrete Lagrangian Neural Networks with Automatic Symmetry Discovery","created":"2022-11-20","cited_by_count":5,"area":null},{"id":"2305.01338","title":"Physics-Informed Learning Using Hamiltonian Neural Networks with Output Error Noise Models","created":"2023-05-02","cited_by_count":5,"area":null},{"id":"2502.16667","title":"MetaSym: A Symplectic Meta-learning Framework for Physical Intelligence","created":"2025-02-23","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":"2209.11355","title":"Learning Interpretable Dynamics from Images of a Freely Rotating 3D Rigid Body","created":"2022-09-23","cited_by_count":4,"area":null},{"id":"2306.03548","title":"Learning Dynamical Systems from Noisy Data with Inverse-Explicit Integrators","created":"2023-06-06","cited_by_count":4,"area":null},{"id":"2308.11216","title":"Hamiltonian GAN","created":"2023-08-22","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":"2408.16698","title":"SympGNNs: Symplectic Graph Neural Networks for identifiying high-dimensional Hamiltonian systems and node classification","created":"2024-08-29","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":"2102.09589","title":"A Differential Geometry Perspective on Orthogonal Recurrent Models","created":"2021-02-18","cited_by_count":3,"area":null},{"id":"2201.08281","title":"Symplectic Momentum Neural Networks -- Using Discrete Variational Mechanics as a prior in Deep Learning","created":"2022-01-20","cited_by_count":3,"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":"2211.06130","title":"Physically Consistent Neural ODEs for Learning Multi-Physics Systems","created":"2022-11-11","cited_by_count":3,"area":null},{"id":"2302.00854","title":"Learning PDE Solution Operator for Continuous Modeling of Time-Series","created":"2023-02-02","cited_by_count":3,"area":null},{"id":"2303.03769","title":"Learning Hamiltonian Systems with Mono-Implicit Runge-Kutta Methods","created":"2023-03-07","cited_by_count":3,"area":null},{"id":"2306.02192","title":"Correcting Auto-Differentiation in Neural-ODE Training","created":"2023-06-03","cited_by_count":3,"area":null},{"id":"2310.07485","title":"Nonlinear embeddings for conserving Hamiltonians and other quantities with Neural Galerkin schemes","created":"2023-10-11","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":"2410.05445","title":"Data-Driven Discovery of Conservation Laws from Trajectories via Neural Deflation","created":"2024-10-07","cited_by_count":3,"area":null},{"id":"2507.15678","title":"GeoHNNs: Geometric Hamiltonian Neural Networks","created":"2025-07-21","cited_by_count":3,"area":null},{"id":"2508.12569","title":"Data-driven particle dynamics: Structure-preserving coarse-graining for emergent behavior in non-equilibrium systems","created":"2025-08-18","cited_by_count":3,"area":null},{"id":"2102.11923","title":"KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-Zero Training Loss","created":"2021-02-22","cited_by_count":2,"area":null},{"id":"2110.14392","title":"TaylorSwiftNet: Taylor Driven Temporal Modeling for Swift Future Frame Prediction","created":"2021-10-27","cited_by_count":2,"area":null},{"id":"2111.15631","title":"Neural Symplectic Integrator with Hamiltonian Inductive Bias for the Gravitational $N$-body Problem","created":"2021-11-28","cited_by_count":2,"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":"2208.09433","title":"Estimating a potential without the agony of the partition function","created":"2022-08-19","cited_by_count":2,"area":null},{"id":"2305.00723","title":"Predictions Based on Pixel Data: Insights from PDEs and Finite Differences","created":"2023-05-01","cited_by_count":2,"area":null},{"id":"2312.09734","title":"Learning of Hamiltonian Dynamics with Reproducing Kernel Hilbert Spaces","created":"2023-12-15","cited_by_count":2,"area":null},{"id":"2401.12476","title":"Bayesian identification of nonseparable Hamiltonians with multiplicative noise using deep learning and reduced-order modeling","created":"2024-01-23","cited_by_count":2,"area":null},{"id":"2409.11138","title":"Learning Generalized Hamiltonians using fully Symplectic Mappings","created":"2024-09-17","cited_by_count":2,"area":null},{"id":"2411.17511","title":"Training Hamiltonian neural networks without backpropagation","created":"2024-11-26","cited_by_count":2,"area":null}],"next_cursor":"100"}