COGENT: Neural ODE Framework for Long-Term Physical Forecasting on Irregular Geospatial Meshes
Researchers introduced COGENT, a machine learning model combining graph neural networks with Neural Ordinary Differential Equations to forecast physical systems over long time horizons on irregular spatial grids. The approach encodes spatial and temporal information through a graph-based history encoder and models future evolution as a continuous latent dynamical system, allowing predictions at arbitrary future times rather than fixed intervals. The method demonstrated improved stability over existing autoregressive approaches when tested on ice-sheet simulations, suggesting potential applications for climate and geophysical modeling.
COGENT represents a novel architecture for physics-informed machine learning that addresses limitations in existing forecasting methods. The model uses a graph-based history encoder to capture both local spatial interactions and temporal evolution from past system states and forcing fields, producing context vectors that initialize a latent Neural ODE. A key innovation is the ability to generate predictions at arbitrary future times by modeling the forecast trajectory as a continuous dynamical system, rather than being constrained to discrete time steps. The researchers also introduced rollout-horizon sampling and progressive scheduling strategies to stabilize training over long prediction horizons. Evaluation on transient ice-sheet simulations from the Ice-sheet and Sea-level System Model showed improved long-range stability compared to autoregressive graph neural network baselines, suggesting the approach could be valuable for applications requiring stable multi-step predictions on irregular geospatial meshes.
Limitations & open questions
The paper does not discuss computational cost or scalability comparisons with baseline methods, nor does it provide details on hyperparameter sensitivity or failure modes. Additionally, while ice-sheet simulation is presented as a test case, generalization to other physical domains (weather, fluid dynamics, etc.) is not empirically demonstrated.
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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