COGENT: Neural ODE Framework for Long-Term Physical Forecasting on Irregular Geospatial Meshes
Researchers have introduced COGENT, a continuous graph emulator using Neural Ordinary Differential Equations designed for long-term physical forecasting on irregular geospatial meshes. The system encodes historical system states and forcing fields through a graph-based encoder, then models forecast trajectories as continuous latent dynamical systems rather than fixed discrete time steps. This approach could improve the stability and flexibility of machine learning emulators used in complex physical simulations, such as ice-sheet modeling.
COGENT (Continuous Graph Emulators with Neural Ordinary Differential Equations) is a newly proposed machine learning framework for forecasting physical systems on irregular geospatial grids. Unlike conventional autoregressive models that step through fixed time intervals and accumulate errors by repeatedly feeding predictions back into the model, COGENT encodes a history of system states and external forcings into node-wise context vectors via a graph neural network, then uses a Neural ODE to evolve a latent representation continuously through time. This design allows the model to generate predictions at arbitrary future times without being constrained to a predefined temporal discretization. A residual decoder translates the latent trajectories back into physical state predictions, enabling direct multi-step forecasting. To address training instability over long forecast horizons, the authors also introduce rollout-horizon sampling and a progressive scheduling strategy. The framework was evaluated on transient ice-sheet simulations from the Ice-sheet and Sea-level System Model (ISSM), where it demonstrated improved long-range stability compared to autoregressive graph neural network baselines. The authors suggest the methodology is broadly applicable to scalable physical simulation emulation wherever stable long-horizon predictions and flexible temporal querying are required.
What's missing
The paper does not report comparisons against non-graph-based neural emulators or physics-informed neural network baselines, leaving open how COGENT performs relative to a broader landscape of surrogate modeling approaches. Computational cost and scalability to larger or higher-resolution meshes are not quantitatively assessed. The evaluation is limited to a single physical domain (ice-sheet simulation), so generalizability to other geophysical or physical systems remains undemonstrated.
What different sources said
- arXiv cs.LGCenter
COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting
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