COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
arXiv:2606.11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes. COGENT encodes a finite history of system states and associated forcing fields and external forcings with a graph-based history encoder, producing node-wise context vectors that capture both local spatial interactions and temporal evolution. These context vectors initialize and condition a latent Neural Ordinary Differential Equation whose dynamics are driven by interpolated fu
The development of COGENT reflects ongoing advancements in AI and machine learning techniques, particularly neural ordinary differential equations, which are becoming increasingly sophisticated for complex forecasting tasks.
This development is crucial for long-term physical forecasting, offering a significant leap in predicting environmental and physical phenomena with higher accuracy and over extended periods, impacting sectors from climate science to urban planning.
The ability to accurately model and predict long-term physical systems on irregular geospatial meshes will improve preparedness for environmental challenges and optimize resource management, moving beyond short-term predictive models.
- · Climate scientists
- · Urban planners
- · Geospatial data companies
- · Energy sector
- · Traditional statistical modeling approaches
- · Entities reliant on short-term forecasts
More accurate and reliable long-term environmental and physical forecasts become available.
Improved decision-making in infrastructure development, disaster preparedness, and resource allocation based on these enhanced forecasts.
Potential for new industries and services built around advanced predictive modeling, leading to more resilient societies and economies.
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Read at arXiv cs.LG