Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.19390 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866914169423396864 |
|---|---|
| author | Morel, Rudy Ramunno, Francesco Pio Shen, Jeff Bietti, Alberto Cho, Kyunghyun Cranmer, Miles Golkar, Siavash Gugnin, Olexandr Krawezik, Geraud Marwah, Tanya McCabe, Michael Meyer, Lucas Mukhopadhyay, Payel Ohana, Ruben Parker, Liam Qu, Helen Rozet, François Leka, K. D. Lanusse, François Fouhey, David Ho, Shirley |
| author_facet | Morel, Rudy Ramunno, Francesco Pio Shen, Jeff Bietti, Alberto Cho, Kyunghyun Cranmer, Miles Golkar, Siavash Gugnin, Olexandr Krawezik, Geraud Marwah, Tanya McCabe, Michael Meyer, Lucas Mukhopadhyay, Payel Ohana, Ruben Parker, Liam Qu, Helen Rozet, François Leka, K. D. Lanusse, François Fouhey, David Ho, Shirley |
| contents | Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings, the available information at a given time represents only a small fraction of what is needed to predict future states, either due to measurement uncertainty or because only a small fraction of the state can be observed. This is true for example in solar physics, where we can observe the Sun's surface and atmosphere, but its evolution is driven by internal processes for which we lack direct measurements. In this paper, we tackle the probabilistic prediction of partially observable, long-memory dynamical systems, with applications to solar dynamics and the evolution of active regions. We show that standard inference schemes, such as autoregressive rollouts, fail to capture long-range dependencies in the data, largely because they do not integrate past information effectively. To overcome this, we propose a multiscale inference scheme for diffusion models, tailored to physical processes. Our method generates trajectories that are temporally fine-grained near the present and coarser as we move farther away, which enables capturing long-range temporal dependencies without increasing computational cost. When integrated into a diffusion model, we show that our inference scheme significantly reduces the bias of the predicted distributions and improves rollout stability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19390 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme Morel, Rudy Ramunno, Francesco Pio Shen, Jeff Bietti, Alberto Cho, Kyunghyun Cranmer, Miles Golkar, Siavash Gugnin, Olexandr Krawezik, Geraud Marwah, Tanya McCabe, Michael Meyer, Lucas Mukhopadhyay, Payel Ohana, Ruben Parker, Liam Qu, Helen Rozet, François Leka, K. D. Lanusse, François Fouhey, David Ho, Shirley Machine Learning Solar and Stellar Astrophysics Artificial Intelligence Conditional diffusion models provide a natural framework for probabilistic prediction of dynamical systems and have been successfully applied to fluid dynamics and weather prediction. However, in many settings, the available information at a given time represents only a small fraction of what is needed to predict future states, either due to measurement uncertainty or because only a small fraction of the state can be observed. This is true for example in solar physics, where we can observe the Sun's surface and atmosphere, but its evolution is driven by internal processes for which we lack direct measurements. In this paper, we tackle the probabilistic prediction of partially observable, long-memory dynamical systems, with applications to solar dynamics and the evolution of active regions. We show that standard inference schemes, such as autoregressive rollouts, fail to capture long-range dependencies in the data, largely because they do not integrate past information effectively. To overcome this, we propose a multiscale inference scheme for diffusion models, tailored to physical processes. Our method generates trajectories that are temporally fine-grained near the present and coarser as we move farther away, which enables capturing long-range temporal dependencies without increasing computational cost. When integrated into a diffusion model, we show that our inference scheme significantly reduces the bias of the predicted distributions and improves rollout stability. |
| title | Predicting partially observable dynamical systems via diffusion models with a multiscale inference scheme |
| topic | Machine Learning Solar and Stellar Astrophysics Artificial Intelligence |
| url | https://arxiv.org/abs/2511.19390 |