Conference
Training-Free Data Assimilation with GenCast
| Τίτλος: | Training-Free Data Assimilation with GenCast |
|---|---|
| Συγγραφείς: | Savary, Thomas, Rozet, François, Louppe, Gilles |
| Συνεισφορές: | Montefiore Institute - Montefiore Institute of Electrical Engineering and Computer Science - ULiège |
| Πηγή: | Climate Change AI Workshop, San Diego, United States - California [US-CA], 07-12-2025 |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | Computer Science - Learning, Engineering, computing & technology, Computer science, Ingénierie, informatique & technologie, Sciences informatiques |
| Περιγραφή: | Data assimilation is widely used in many disciplines such as meteorology, oceanography, and robotics to estimate the state of a dynamical system from noisy observations. In this work, we propose a lightweight and general method to perform data assimilation using diffusion models pre-trained for emulating dynamical systems. Our method builds on particle filters, a class of data assimilation algorithms, and does not require any further training. As a guiding example throughout this work, we illustrate our methodology on GenCast, a diffusion-based model that generates global ensemble weather forecasts. |
| Τύπος εγγράφου: | conference poster not in proceedings http://purl.org/coar/resource_type/c_18co conferencePoster peer reviewed |
| Γλώσσα: | English |
| Σύνδεσμος πρόσβασης: | https://orbi.uliege.be/handle/2268/340521 |
| Rights: | open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess |
| Αριθμός Καταχώρησης: | edsorb.340521 |
| Βάση Δεδομένων: | ORBi |
| Η περιγραφή δεν είναι διαθέσιμη |