{"total":45,"rows":[{"id":"2311.04128","title":"Generative learning for nonlinear dynamics","created":"2023-11-07","cited_by_count":22,"area":null},{"id":"2205.00579","title":"Data-driven control of spatiotemporal chaos with reduced-order neural ODE-based models and reinforcement learning","created":"2022-05-01","cited_by_count":17,"area":null},{"id":"2303.15244","title":"Manifold Learning by Mixture Models of VAEs for Inverse Problems","created":"2023-03-27","cited_by_count":17,"area":null},{"id":"2305.01090","title":"Autoencoders for discovering manifold dimension and coordinates in data from complex dynamical systems","created":"2023-05-01","cited_by_count":13,"area":null},{"id":"2304.03376","title":"Interpretable statistical representations of neural population dynamics and geometry","created":"2023-04-06","cited_by_count":12,"area":null},{"id":"2309.05812","title":"Interpretable learning of effective dynamics for multiscale systems","created":"2023-09-11","cited_by_count":11,"area":null},{"id":"2411.19305","title":"LD-EnSF: Synergizing Latent Dynamics with Ensemble Score Filters for Fast Data Assimilation with Sparse Observations","created":"2024-11-28","cited_by_count":11,"area":null},{"id":"2210.16708","title":"Data-driven low-dimensional dynamic model of Kolmogorov flow","created":"2022-10-29","cited_by_count":9,"area":null},{"id":"2405.20905","title":"VENI, VINDy, VICI: a generative reduced-order modeling framework with uncertainty quantification","created":"2024-05-31","cited_by_count":9,"area":null},{"id":"2208.10570","title":"Semi-Supervised Manifold Learning with Complexity Decoupled Chart Autoencoders","created":"2022-08-22","cited_by_count":8,"area":null},{"id":"2211.11061","title":"Deep learning delay coordinate dynamics for chaotic attractors from partial observable data","created":"2022-11-20","cited_by_count":8,"area":null},{"id":"2211.15145","title":"Emerging trends in machine learning for computational fluid dynamics","created":"2022-11-28","cited_by_count":8,"area":null},{"id":"2301.04638","title":"Dynamics of a data-driven low-dimensional model of turbulent minimal Couette flow","created":"2023-01-11","cited_by_count":8,"area":null},{"id":"2310.16741","title":"Stochastic Latent Transformer: Efficient Modelling of Stochastically Forced Zonal Jets","created":"2023-10-25","cited_by_count":8,"area":null},{"id":"2505.12083","title":"Discovering Symbolic Differential Equations with Symmetry Invariants","created":"2025-05-17","cited_by_count":6,"area":null},{"id":"2301.12098","title":"Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning","created":"2023-01-28","cited_by_count":5,"area":null},{"id":"2304.01732","title":"Adaptive learning of effective dynamics: Adaptive real-time, online modeling for complex systems","created":"2023-04-04","cited_by_count":4,"area":null},{"id":"2305.00094","title":"Latent Dynamics Networks (LDNets): learning the intrinsic dynamics of spatio-temporal processes","created":"2023-04-28","cited_by_count":4,"area":null},{"id":"2308.09952","title":"Finding emergence in data by maximizing effective information","created":"2023-08-19","cited_by_count":4,"area":null},{"id":"2411.01719","title":"LES-SINDy: Laplace-Enhanced Sparse Identification of Nonlinear Dynamical Systems","created":"2024-11-04","cited_by_count":4,"area":null},{"id":"2412.20962","title":"Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction","created":"2024-12-30","cited_by_count":4,"area":null},{"id":"2309.14334","title":"Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points","created":"2023-09-25","cited_by_count":3,"area":null},{"id":"2401.01100","title":"Sampling-enabled scalable manifold learning unveils the discriminative cluster structure of high-dimensional data","created":"2024-01-02","cited_by_count":3,"area":null},{"id":"2512.17203","title":"Learning solution operator of dynamical systems with diffusion maps kernel ridge regression","created":"2025-12-19","cited_by_count":3,"area":null},{"id":"2310.16466","title":"Learning Continuous Network Emerging Dynamics from Scarce Observations via Data-Adaptive Stochastic Processes","created":"2023-10-25","cited_by_count":2,"area":null},{"id":"2312.10235","title":"Building symmetries into data-driven manifold dynamics models for complex flows: application to two-dimensional Kolmogorov flow","created":"2023-12-15","cited_by_count":2,"area":null},{"id":"2401.13282","title":"RefreshNet: Learning Multiscale Dynamics through Hierarchical Refreshing","created":"2024-01-24","cited_by_count":2,"area":null},{"id":"2406.13101","title":"On instabilities in neural network-based physics simulators","created":"2024-06-18","cited_by_count":2,"area":null},{"id":"2501.12582","title":"Ultralow-dimensionality reduction for identifying critical transitions by spatial-temporal PCA","created":"2025-01-22","cited_by_count":2,"area":null},{"id":"2510.04138","title":"Efficient Manifold-Constrained Neural ODE for High-Dimensional Datasets","created":"2025-10-05","cited_by_count":2,"area":null},{"id":"2604.16282","title":"Geometric regularization of autoencoders via observed stochastic dynamics","created":"2026-04-17","cited_by_count":2,"area":null},{"id":"2404.17535","title":"Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems","created":"2024-04-26","cited_by_count":1,"area":null},{"id":"2511.06609","title":"A Weak Penalty Neural ODE for Learning Chaotic Dynamics from Noisy Time Series","created":"2025-11-10","cited_by_count":1,"area":null},{"id":"2607.00257","title":"Learning dynamical systems from noisy data with Weak-form Kernel Ridge Regression","created":"2026-06-30","cited_by_count":1,"area":null},{"id":"2308.02621","title":"Color Image Recovery Using Generalized Matrix Completion over Higher-Order Finite Dimensional Algebra","created":"2023-08-04","cited_by_count":0,"area":null},{"id":"2401.05226","title":"Learning effective good variables from physical data","created":"2024-01-10","cited_by_count":0,"area":null},{"id":"2404.17865","title":"Vision-based Discovery of Nonlinear Dynamics for 3D Moving Target","created":"2024-04-27","cited_by_count":0,"area":null},{"id":"2506.06308","title":"Scientific machine learning in Hydrology: a unified perspective","created":"2025-05-24","cited_by_count":0,"area":null},{"id":"2507.21299","title":"Blending data and physics for reduced-order modeling of systems with spatiotemporal chaotic dynamics","created":"2025-07-21","cited_by_count":0,"area":null},{"id":"2509.15517","title":"A Survey and Comparative Evaluation of Intrinsic Dimension Estimators under the Manifold Hypothesis","created":"2025-09-19","cited_by_count":0,"area":null},{"id":"2511.15822","title":"Atlas Gaussian processes on restricted domains and point clouds","created":"2025-11-19","cited_by_count":0,"area":null},{"id":"2602.03319","title":"Information-Theoretic Multi-Model Fusion for Target-Oriented Adaptive Sampling in Materials Design","created":"2026-02-03","cited_by_count":0,"area":null},{"id":"2603.22380","title":"Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data","created":"2026-03-23","cited_by_count":0,"area":null},{"id":"2604.19465","title":"A neural operator framework for data-driven discovery of stability and receptivity in physical systems","created":"2026-04-21","cited_by_count":0,"area":null},{"id":"2607.00669","title":"Convolutional Symmetric AutoEncoders: enhancing latent stability via differential geometry","created":"2026-07-01","cited_by_count":0,"area":null}],"next_cursor":null}