{"total":23,"rows":[{"id":"2303.10462","title":"Machine learning with data assimilation and uncertainty quantification for dynamical systems: a review","created":"2023-03-18","cited_by_count":75,"area":null},{"id":"2404.06665","title":"Deep Generative Data Assimilation in Multimodal Setting","created":"2024-04-10","cited_by_count":29,"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":"2409.00127","title":"Latent-EnSF: A Latent Ensemble Score Filter for High-Dimensional Data Assimilation with Sparse Observation Data","created":"2024-08-29","cited_by_count":22,"area":null},{"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":"2310.16187","title":"Efficient deep data assimilation with sparse observations and time-varying sensors","created":"2023-10-24","cited_by_count":14,"area":null},{"id":"2502.09346","title":"Machine learning for modelling unstructured grid data in computational physics: a review","created":"2025-02-13","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":"2308.02810","title":"A generative model for surrogates of spatial-temporal wildfire nowcasting","created":"2023-08-05","cited_by_count":8,"area":null},{"id":"2402.02031","title":"Multi-fidelity physics constrained neural networks for dynamical systems","created":"2024-02-03","cited_by_count":5,"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":"2605.05586","title":"AeroJEPA: Learning Semantic Latent Representations for Scalable 3D Aerodynamic Field Modeling","created":"2026-05-07","cited_by_count":4,"area":null},{"id":"2406.02204","title":"The Deep Latent Space Particle Filter for Real-Time Data Assimilation with Uncertainty Quantification","created":"2024-06-04","cited_by_count":3,"area":null},{"id":"2510.13030","title":"Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators","created":"2025-10-14","cited_by_count":3,"area":null},{"id":"2603.06752","title":"Latent Autoencoder Ensemble Kalman Filter for Nonlinear Data assimilation","created":"2026-03-06","cited_by_count":3,"area":null},{"id":"2303.08455","title":"On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem","created":"2023-03-15","cited_by_count":2,"area":null},{"id":"2307.04216","title":"Hierarchical Autoencoder-based Lossy Compression for Large-scale High-resolution Scientific Data","created":"2023-07-09","cited_by_count":1,"area":null},{"id":"2308.08936","title":"Estimating fire Duration using regression methods","created":"2023-08-17","cited_by_count":1,"area":null},{"id":"2603.25597","title":"Spatiotemporal System Forecasting with Irregular Time Steps via Masked Autoencoder","created":"2026-03-26","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":"2608.11435","title":"Variational Parameter Calibration with Physics-Aware Latent-Space Surrogates","created":"2026-08-11","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}