HAL (France)
open access
Improving long-term autoregressive spatiotemporal predictions: A proof of concept with fluid dynamics
a b s t r a c t Data-driven approaches have emerged as a powerful alternative to traditional numerical methods for forecasting physical systems, offering fast inference and reduced computational costs. However, for complex systems and those without prior knowledge, the accuracy of long-term
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Explanation of Dynamic Physical Field Predictions using WassersteinGrad: Application to Autoregressive Weather Forecasting#134545
- Physics Foundation Models for Efficient Control of Fluid Flows#1326423
- Distillation of Synthetic Data for Time Series Foundation Models#1084402
- Temporal Predictive Multiplicity: Equally Accurate Time Series Models Yield Different Forecast Trajectories#1234361
- DAWIS: Data Assimilation with Windowed Inverse Sampling via Multitask Interpolants#1187992
Record · ID 1459774
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive