A Score Filter Enhanced Data Assimilation Framework for Data-Driven Dynamical Systems
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866915865477251072 |
|---|---|
| author | Tang, Jingqiao Bausback, Ryan Bao, Feng Zhang, Guannan Huynh, Phuoc-Toan |
| author_facet | Tang, Jingqiao Bausback, Ryan Bao, Feng Zhang, Guannan Huynh, Phuoc-Toan |
| contents | We introduce a score-filter-enhanced data assimilation framework designed to reduce predictive uncertainty in machine learning (ML) models for data-driven dynamical system forecasting. Machine learning serves as an efficient numerical model for predicting dynamical systems. However, even with sufficient data, model uncertainty remains and accumulates over time, causing the long-term performance of ML models to deteriorate. To overcome this difficulty, we integrate data assimilation techniques into the training process to iteratively refine the model predictions by incorporating observational information. Specifically, we apply the Ensemble Score Filter (EnSF), a generative AI-based training-free diffusion model approach, for solving the data assimilation problem in high-dimensional nonlinear complex systems. This leads to a hybrid data assimilation-training framework that combines ML with EnSF to improve long-term predictive performance. We shall demonstrate that EnSF-enhanced ML can effectively reduce predictive uncertainty in ML-based Lorenz-96 system prediction and the Korteweg-De Vries (KdV) equation prediction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_14863 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Score Filter Enhanced Data Assimilation Framework for Data-Driven Dynamical Systems Tang, Jingqiao Bausback, Ryan Bao, Feng Zhang, Guannan Huynh, Phuoc-Toan Dynamical Systems Machine Learning Probability We introduce a score-filter-enhanced data assimilation framework designed to reduce predictive uncertainty in machine learning (ML) models for data-driven dynamical system forecasting. Machine learning serves as an efficient numerical model for predicting dynamical systems. However, even with sufficient data, model uncertainty remains and accumulates over time, causing the long-term performance of ML models to deteriorate. To overcome this difficulty, we integrate data assimilation techniques into the training process to iteratively refine the model predictions by incorporating observational information. Specifically, we apply the Ensemble Score Filter (EnSF), a generative AI-based training-free diffusion model approach, for solving the data assimilation problem in high-dimensional nonlinear complex systems. This leads to a hybrid data assimilation-training framework that combines ML with EnSF to improve long-term predictive performance. We shall demonstrate that EnSF-enhanced ML can effectively reduce predictive uncertainty in ML-based Lorenz-96 system prediction and the Korteweg-De Vries (KdV) equation prediction. |
| title | A Score Filter Enhanced Data Assimilation Framework for Data-Driven Dynamical Systems |
| topic | Dynamical Systems Machine Learning Probability |
| url | https://arxiv.org/abs/2603.14863 |