{"total":19,"rows":[{"id":"2312.12462","title":"Towards an end-to-end artificial intelligence driven global weather forecasting system","created":"2023-12-18","cited_by_count":21,"area":null},{"id":"2206.04811","title":"Deep learning-enhanced ensemble-based data assimilation for high-dimensional nonlinear dynamical systems","created":"2022-06-09","cited_by_count":12,"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":"2309.09574","title":"Latent assimilation with implicit neural representations for unknown dynamics","created":"2023-09-18","cited_by_count":9,"area":null},{"id":"2406.01645","title":"FNP: Fourier Neural Processes for Arbitrary-Resolution Data Assimilation","created":"2024-06-03","cited_by_count":8,"area":null},{"id":"2501.06661","title":"Learning dynamical systems with hit-and-run random feature maps","created":"2025-01-11","cited_by_count":8,"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":"2408.02767","title":"4D-Var using Hessian approximation and backpropagation applied to automatically-differentiable numerical and machine learning models","created":"2024-08-05","cited_by_count":6,"area":null},{"id":"2409.00244","title":"TorchDA: A Python package for performing data assimilation with deep learning forward and transformation functions","created":"2024-08-30","cited_by_count":5,"area":null},{"id":"2201.05193","title":"`Next Generation' Reservoir Computing: an Empirical Data-Driven Expression of Dynamical Equations in Time-Stepping Form","created":"2022-01-13","cited_by_count":3,"area":null},{"id":"2305.00100","title":"Temporal Subsampling Diminishes Small Spatial Scales in Recurrent Neural Network Emulators of Geophysical Turbulence","created":"2023-04-28","cited_by_count":3,"area":null},{"id":"2409.07137","title":"Combined Optimization of Dynamics and Assimilation with End-to-End Learning on Sparse Observations","created":"2024-09-11","cited_by_count":3,"area":null},{"id":"2407.17781","title":"Ensemble data assimilation to diagnose AI-based weather prediction model: A case with ClimaX version 0.3.1","created":"2024-07-25","cited_by_count":1,"area":null},{"id":"2412.14318","title":"Long-time accuracy of ensemble Kalman filters for chaotic and machine-learned dynamical systems","created":"2024-12-18","cited_by_count":1,"area":null},{"id":"2608.23297","title":"ADDA: a Modular Framework for Representing, Simulating and Assimilating Dynamics with End-to-end Differentiability","created":"2026-08-24","cited_by_count":1,"area":null},{"id":"2403.12366","title":"U-Net Kalman Filter (UNetKF): An Example of Machine Learning-assisted Ensemble Data Assimilation","created":"2024-03-19","cited_by_count":0,"area":null},{"id":"2504.16192","title":"Probabilistic Emulation of the Community Radiative Transfer Model Using Machine Learning","created":"2025-04-22","cited_by_count":0,"area":null},{"id":"2607.12975","title":"Ensemble Controlled-Flow Filtering for Implicit Data Assimilation","created":"2026-07-14","cited_by_count":0,"area":null},{"id":"2610.04034","title":"LD-EnFF: Latent-Dynamics Ensemble Flow Filtering for Data Assimilation with Sparse Observations","created":"2026-10-02","cited_by_count":0,"area":null}],"next_cursor":null}